Summary of All Tables

Table Rows Columns % Missing (max col) % Duplicates
customer 30 19 100.00 3.33
employee_privileges 1 3 0.00 0.00
employees 9 19 100.00 0.00
inventory_transaction_types 4 3 0.00 0.00
inventory_transactions 102 10 100.00 0.00
invoices 35 8 100.00 0.00
order_details 58 11 100.00 0.00
order_details_status 6 3 0.00 0.00
orders 48 21 100.00 0.00
orders_status 4 3 0.00 0.00
orders_tax_status 2 3 0.00 0.00
privileges 1 3 0.00 0.00
products 45 15 100.00 0.00
purchase_order_details 55 9 21.82 0.00
purchase_order_status 4 3 0.00 0.00
purchase_orders 28 17 100.00 0.00
sales_reports 5 6 0.00 0.00
shippers 3 19 100.00 0.00
strings 62 3 0.00 0.00
suppliers 10 19 100.00 0.00

Table: customer

Profile: customer

Overview

Brought to you by YData

Dataset statistics
Number of variables19
Number of observations30
Missing cells150
Missing cells (%)26.3%
Total size in memory28.8 KiB
Average record size in memory982.8 B

Variable types
Numeric1
Text13
Unsupported5

Alerts

business_phone has constant value "(123)555-0100"Constant
fax_number has constant value "(123)555-0101"Constant
zip_postal_code has constant value "99999"Constant
country_region has constant value "USA"Constant
attachments has constant value ""Constant
__table_name__ has constant value "customer"Constant
email_address has 30 (100.0%) missing valuesMissing
home_phone has 30 (100.0%) missing valuesMissing
mobile_phone has 30 (100.0%) missing valuesMissing
web_page has 30 (100.0%) missing valuesMissing
notes has 30 (100.0%) missing valuesMissing
email_address is an unsupported type, check if it needs cleaning or further analysisUnsupported
home_phone is an unsupported type, check if it needs cleaning or further analysisUnsupported
mobile_phone is an unsupported type, check if it needs cleaning or further analysisUnsupported
web_page is an unsupported type, check if it needs cleaning or further analysisUnsupported
notes is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction
Analysis started2025-10-30 16:47:01.998771
Analysis finished2025-10-30 16:47:02.138495
Duration0.14 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.53333333
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2025-10-30T18:47:02.232693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum1
5-th percentile1.45
Q17.25
median14.5
Q321.75
95-th percentile27.55
Maximum29
Range28
Interquartile range (IQR)14.5

Descriptive statistics
Standard deviation8.748333175
Coefficient of variation (CV)0.6019495304
Kurtosis-1.225719021
Mean14.53333333
Median Absolute Deviation (MAD)7.5
Skewness0.02021290968
Sum436
Variance76.53333333
MonotonicityNot monotonic

2025-10-30T18:47:02.315969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
12
 
6.7%
191
 
3.3%
161
 
3.3%
141
 
3.3%
31
 
3.3%
261
 
3.3%
111
 
3.3%
151
 
3.3%
71
 
3.3%
251
 
3.3%
Other values (19)19
63.3%
ValueCountFrequency (%)
12
6.7%
21
3.3%
31
3.3%
41
3.3%
51
3.3%
ValueCountFrequency (%)
291
3.3%
281
3.3%
271
3.3%
261
3.3%
251
3.3%

company
Text

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2025-10-30T18:47:02.400189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length10
Median length9
Mean length9.1
Min length9

Characters and Unicode
Total characters273
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique28 ?
Unique (%)93.3%

Sample
1st rowCompany C
2nd rowCompany S
3rd rowCompany P
4th rowCompany N
5th rowCompany CC

ValueCountFrequency (%)
company30
50.0%
a2
 
3.3%
s1
 
1.7%
p1
 
1.7%
n1
 
1.7%
c1
 
1.7%
z1
 
1.7%
k1
 
1.7%
o1
 
1.7%
g1
 
1.7%
Other values (20)20
33.3%
2025-10-30T18:47:02.564818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C33
12.1%
o30
11.0%
m30
11.0%
p30
11.0%
a30
11.0%
n30
11.0%
y30
11.0%
30
11.0%
A4
 
1.5%
B3
 
1.1%
Other values (23)23
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)273
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C33
12.1%
o30
11.0%
m30
11.0%
p30
11.0%
a30
11.0%
n30
11.0%
y30
11.0%
30
11.0%
A4
 
1.5%
B3
 
1.1%
Other values (23)23
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)273
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C33
12.1%
o30
11.0%
m30
11.0%
p30
11.0%
a30
11.0%
n30
11.0%
y30
11.0%
30
11.0%
A4
 
1.5%
B3
 
1.1%
Other values (23)23
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)273
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C33
12.1%
o30
11.0%
m30
11.0%
p30
11.0%
a30
11.0%
n30
11.0%
y30
11.0%
30
11.0%
A4
 
1.5%
B3
 
1.1%
Other values (23)23
8.4%
Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2025-10-30T18:47:02.663568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length16
Median length11.5
Mean length6.566666667
Min length2

Characters and Unicode
Total characters197
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique26 ?
Unique (%)86.7%

Sample
1st rowAxen
2nd rowEggerer
3rd rowGoldschmidt
4th rowGrilo
5th rowLee

ValueCountFrequency (%)
lee2
 
6.2%
bedecs2
 
6.2%
goldschmidt1
 
3.1%
axen1
 
3.1%
grilo1
 
3.1%
liu1
 
3.1%
krschne1
 
3.1%
kupkova1
 
3.1%
xie1
 
3.1%
rodman1
 
3.1%
Other values (20)20
62.5%
2025-10-30T18:47:02.817973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e25
 
12.7%
a15
 
7.6%
n13
 
6.6%
r12
 
6.1%
o12
 
6.1%
s12
 
6.1%
i10
 
5.1%
d9
 
4.6%
l8
 
4.1%
c8
 
4.1%
Other values (32)73
37.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)197
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e25
 
12.7%
a15
 
7.6%
n13
 
6.6%
r12
 
6.1%
o12
 
6.1%
s12
 
6.1%
i10
 
5.1%
d9
 
4.6%
l8
 
4.1%
c8
 
4.1%
Other values (32)73
37.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)197
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e25
 
12.7%
a15
 
7.6%
n13
 
6.6%
r12
 
6.1%
o12
 
6.1%
s12
 
6.1%
i10
 
5.1%
d9
 
4.6%
l8
 
4.1%
c8
 
4.1%
Other values (32)73
37.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)197
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e25
 
12.7%
a15
 
7.6%
n13
 
6.6%
r12
 
6.1%
o12
 
6.1%
s12
 
6.1%
i10
 
5.1%
d9
 
4.6%
l8
 
4.1%
c8
 
4.1%
Other values (32)73
37.1%
Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2025-10-30T18:47:02.900487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length13
Median length8
Mean length6.566666667
Min length3

Characters and Unicode
Total characters197
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique26 ?
Unique (%)86.7%

Sample
1st rowThomas
2nd rowAlexander
3rd rowDaniel
4th rowCarlos
5th rowSoo Jung

ValueCountFrequency (%)
john2
 
6.2%
anna2
 
6.2%
daniel1
 
3.1%
thomas1
 
3.1%
carlos1
 
3.1%
soo1
 
3.1%
jung1
 
3.1%
peter1
 
3.1%
run1
 
3.1%
helena1
 
3.1%
Other values (20)20
62.5%
2025-10-30T18:47:03.045147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n28
14.2%
a22
 
11.2%
e19
 
9.6%
i14
 
7.1%
r13
 
6.6%
o12
 
6.1%
h9
 
4.6%
l8
 
4.1%
t7
 
3.6%
A6
 
3.0%
Other values (29)59
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)197
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n28
14.2%
a22
 
11.2%
e19
 
9.6%
i14
 
7.1%
r13
 
6.6%
o12
 
6.1%
h9
 
4.6%
l8
 
4.1%
t7
 
3.6%
A6
 
3.0%
Other values (29)59
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)197
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n28
14.2%
a22
 
11.2%
e19
 
9.6%
i14
 
7.1%
r13
 
6.6%
o12
 
6.1%
h9
 
4.6%
l8
 
4.1%
t7
 
3.6%
A6
 
3.0%
Other values (29)59
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)197
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n28
14.2%
a22
 
11.2%
e19
 
9.6%
i14
 
7.1%
r13
 
6.6%
o12
 
6.1%
h9
 
4.6%
l8
 
4.1%
t7
 
3.6%
A6
 
3.0%
Other values (29)59
29.9%

email_address
Unsupported

Missing  Rejected  Unsupported 

Missing30
Missing (%)100.0%
Memory size368.0 B
Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2025-10-30T18:47:03.108671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length25
Median length20
Mean length16.56666667
Min length5

Characters and Unicode
Total characters497
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique2 ?
Unique (%)6.7%

Sample
1st rowPurchasing Representative
2nd rowAccounting Assistant
3rd rowPurchasing Representative
4th rowPurchasing Representative
5th rowPurchasing Manager

ValueCountFrequency (%)
purchasing20
37.7%
manager14
26.4%
owner7
 
13.2%
representative6
 
11.3%
accounting3
 
5.7%
assistant3
 
5.7%
2025-10-30T18:47:03.251804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a57
11.5%
n56
11.3%
r47
9.5%
e45
 
9.1%
g37
 
7.4%
s35
 
7.0%
i32
 
6.4%
c26
 
5.2%
u23
 
4.6%
23
 
4.6%
Other values (11)116
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)497
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a57
11.5%
n56
11.3%
r47
9.5%
e45
 
9.1%
g37
 
7.4%
s35
 
7.0%
i32
 
6.4%
c26
 
5.2%
u23
 
4.6%
23
 
4.6%
Other values (11)116
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)497
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a57
11.5%
n56
11.3%
r47
9.5%
e45
 
9.1%
g37
 
7.4%
s35
 
7.0%
i32
 
6.4%
c26
 
5.2%
u23
 
4.6%
23
 
4.6%
Other values (11)116
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)497
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a57
11.5%
n56
11.3%
r47
9.5%
e45
 
9.1%
g37
 
7.4%
s35
 
7.0%
i32
 
6.4%
c26
 
5.2%
u23
 
4.6%
23
 
4.6%
Other values (11)116
23.3%

business_phone
Text

Constant 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2025-10-30T18:47:03.311718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length13
Median length13
Mean length13
Min length13

Characters and Unicode
Total characters390
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st row(123)555-0100
2nd row(123)555-0100
3rd row(123)555-0100
4th row(123)555-0100
5th row(123)555-0100

ValueCountFrequency (%)
123)555-010030
100.0%
2025-10-30T18:47:03.410805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
090
23.1%
590
23.1%
160
15.4%
(30
 
7.7%
330
 
7.7%
230
 
7.7%
)30
 
7.7%
-30
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
090
23.1%
590
23.1%
160
15.4%
(30
 
7.7%
330
 
7.7%
230
 
7.7%
)30
 
7.7%
-30
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
090
23.1%
590
23.1%
160
15.4%
(30
 
7.7%
330
 
7.7%
230
 
7.7%
)30
 
7.7%
-30
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
090
23.1%
590
23.1%
160
15.4%
(30
 
7.7%
330
 
7.7%
230
 
7.7%
)30
 
7.7%
-30
 
7.7%

home_phone
Unsupported

Missing  Rejected  Unsupported 

Missing30
Missing (%)100.0%
Memory size368.0 B

mobile_phone
Unsupported

Missing  Rejected  Unsupported 

Missing30
Missing (%)100.0%
Memory size368.0 B

fax_number
Text

Constant 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2025-10-30T18:47:03.473888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length13
Median length13
Mean length13
Min length13

Characters and Unicode
Total characters390
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st row(123)555-0101
2nd row(123)555-0101
3rd row(123)555-0101
4th row(123)555-0101
5th row(123)555-0101

ValueCountFrequency (%)
123)555-010130
100.0%
2025-10-30T18:47:03.609486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
190
23.1%
590
23.1%
060
15.4%
(30
 
7.7%
330
 
7.7%
230
 
7.7%
)30
 
7.7%
-30
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
190
23.1%
590
23.1%
060
15.4%
(30
 
7.7%
330
 
7.7%
230
 
7.7%
)30
 
7.7%
-30
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
190
23.1%
590
23.1%
060
15.4%
(30
 
7.7%
330
 
7.7%
230
 
7.7%
)30
 
7.7%
-30
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
190
23.1%
590
23.1%
060
15.4%
(30
 
7.7%
330
 
7.7%
230
 
7.7%
)30
 
7.7%
-30
 
7.7%

address
Text

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2025-10-30T18:47:03.688486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length15
Median length15
Mean length14.66666667
Min length14

Characters and Unicode
Total characters440
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique28 ?
Unique (%)93.3%

Sample
1st row123 3rd Street
2nd row789 19th Street
3rd row456 16th Street
4th row456 14th Street
5th row789 29th Street

ValueCountFrequency (%)
street30
33.3%
12313
14.4%
78911
 
12.2%
4566
 
6.7%
1st2
 
2.2%
19th1
 
1.1%
16th1
 
1.1%
14th1
 
1.1%
3rd1
 
1.1%
26th1
 
1.1%
Other values (23)23
25.6%
2025-10-30T18:47:03.950207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t88
20.0%
60
13.6%
e60
13.6%
r31
 
7.0%
S30
 
6.8%
127
 
6.1%
226
 
5.9%
h26
 
5.9%
316
 
3.6%
814
 
3.2%
Other values (9)62
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)440
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t88
20.0%
60
13.6%
e60
13.6%
r31
 
7.0%
S30
 
6.8%
127
 
6.1%
226
 
5.9%
h26
 
5.9%
316
 
3.6%
814
 
3.2%
Other values (9)62
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)440
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t88
20.0%
60
13.6%
e60
13.6%
r31
 
7.0%
S30
 
6.8%
127
 
6.1%
226
 
5.9%
h26
 
5.9%
316
 
3.6%
814
 
3.2%
Other values (9)62
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)440
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t88
20.0%
60
13.6%
e60
13.6%
r31
 
7.0%
S30
 
6.8%
127
 
6.1%
226
 
5.9%
h26
 
5.9%
316
 
3.6%
814
 
3.2%
Other values (9)62
14.1%

city
Text

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2025-10-30T18:47:04.048189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length14
Median length11
Mean length8.3
Min length5

Characters and Unicode
Total characters249
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique3 ?
Unique (%)10.0%

Sample
1st rowLos Angelas
2nd rowLos Angelas
3rd rowSan Francisco
4th rowDenver
5th rowDenver

ValueCountFrequency (%)
seattle3
 
7.3%
los2
 
4.9%
angelas2
 
4.9%
miami2
 
4.9%
denver2
 
4.9%
chicago2
 
4.9%
new2
 
4.9%
york2
 
4.9%
minneapolis2
 
4.9%
boston2
 
4.9%
Other values (12)20
48.8%
2025-10-30T18:47:04.238717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e27
 
10.8%
a25
 
10.0%
i18
 
7.2%
o18
 
7.2%
s16
 
6.4%
n15
 
6.0%
l15
 
6.0%
t14
 
5.6%
11
 
4.4%
M8
 
3.2%
Other values (24)82
32.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e27
 
10.8%
a25
 
10.0%
i18
 
7.2%
o18
 
7.2%
s16
 
6.4%
n15
 
6.0%
l15
 
6.0%
t14
 
5.6%
11
 
4.4%
M8
 
3.2%
Other values (24)82
32.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e27
 
10.8%
a25
 
10.0%
i18
 
7.2%
o18
 
7.2%
s16
 
6.4%
n15
 
6.0%
l15
 
6.0%
t14
 
5.6%
11
 
4.4%
M8
 
3.2%
Other values (24)82
32.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e27
 
10.8%
a25
 
10.0%
i18
 
7.2%
o18
 
7.2%
s16
 
6.4%
n15
 
6.0%
l15
 
6.0%
t14
 
5.6%
11
 
4.4%
M8
 
3.2%
Other values (24)82
32.9%
Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2025-10-30T18:47:04.321150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length2
Median length2
Mean length2
Min length2

Characters and Unicode
Total characters60
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique2 ?
Unique (%)6.7%

Sample
1st rowCA
2nd rowCA
3rd rowCA
4th rowCO
5th rowCO

ValueCountFrequency (%)
ca3
 
10.0%
wa3
 
10.0%
co2
 
6.7%
ma2
 
6.7%
il2
 
6.7%
mn2
 
6.7%
fl2
 
6.7%
or2
 
6.7%
tn2
 
6.7%
ny2
 
6.7%
Other values (5)8
26.7%
2025-10-30T18:47:04.489114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A8
13.3%
N8
13.3%
I6
10.0%
C5
8.3%
W5
8.3%
O4
 
6.7%
M4
 
6.7%
L4
 
6.7%
T4
 
6.7%
F2
 
3.3%
Other values (6)10
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)60
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A8
13.3%
N8
13.3%
I6
10.0%
C5
8.3%
W5
8.3%
O4
 
6.7%
M4
 
6.7%
L4
 
6.7%
T4
 
6.7%
F2
 
3.3%
Other values (6)10
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)60
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A8
13.3%
N8
13.3%
I6
10.0%
C5
8.3%
W5
8.3%
O4
 
6.7%
M4
 
6.7%
L4
 
6.7%
T4
 
6.7%
F2
 
3.3%
Other values (6)10
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)60
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A8
13.3%
N8
13.3%
I6
10.0%
C5
8.3%
W5
8.3%
O4
 
6.7%
M4
 
6.7%
L4
 
6.7%
T4
 
6.7%
F2
 
3.3%
Other values (6)10
16.7%

zip_postal_code
Text

Constant 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2025-10-30T18:47:04.544871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length5
Median length5
Mean length5
Min length5

Characters and Unicode
Total characters150
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st row99999
2nd row99999
3rd row99999
4th row99999
5th row99999

ValueCountFrequency (%)
9999930
100.0%
2025-10-30T18:47:04.748000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9150
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9150
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9150
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9150
100.0%

country_region
Text

Constant 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2025-10-30T18:47:04.812949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length3
Median length3
Mean length3
Min length3

Characters and Unicode
Total characters90
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowUSA
2nd rowUSA
3rd rowUSA
4th rowUSA
5th rowUSA

ValueCountFrequency (%)
usa30
100.0%
2025-10-30T18:47:04.982027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U30
33.3%
S30
33.3%
A30
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)90
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U30
33.3%
S30
33.3%
A30
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)90
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U30
33.3%
S30
33.3%
A30
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)90
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U30
33.3%
S30
33.3%
A30
33.3%

web_page
Unsupported

Missing  Rejected  Unsupported 

Missing30
Missing (%)100.0%
Memory size368.0 B

notes
Unsupported

Missing  Rejected  Unsupported 

Missing30
Missing (%)100.0%
Memory size368.0 B

attachments
Text

Constant 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB

Length
Max length0
Median length0
Mean length0
Min length0

Characters and Unicode
Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st row
2nd row
3rd row
4th row
5th row

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

__table_name__
Text

Constant 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2025-10-30T18:47:05.123022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length8
Median length8
Mean length8
Min length8

Characters and Unicode
Total characters240
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowcustomer
2nd rowcustomer
3rd rowcustomer
4th rowcustomer
5th rowcustomer

ValueCountFrequency (%)
customer30
100.0%
2025-10-30T18:47:05.283253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c30
12.5%
u30
12.5%
s30
12.5%
t30
12.5%
o30
12.5%
m30
12.5%
e30
12.5%
r30
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c30
12.5%
u30
12.5%
s30
12.5%
t30
12.5%
o30
12.5%
m30
12.5%
e30
12.5%
r30
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c30
12.5%
u30
12.5%
s30
12.5%
t30
12.5%
o30
12.5%
m30
12.5%
e30
12.5%
r30
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c30
12.5%
u30
12.5%
s30
12.5%
t30
12.5%
o30
12.5%
m30
12.5%
e30
12.5%
r30
12.5%

Table: employee_privileges

Profile: employee_privileges

Overview

Brought to you by YData

Dataset statistics
Number of variables3
Number of observations1
Missing cells0
Missing cells (%)0.0%
Total size in memory222.0 B
Average record size in memory222.0 B

Variable types
Numeric2
Text1

Alerts

employee_id has constant value "2"Constant
privilege_id has constant value "2"Constant
__table_name__ has constant value "employee_privileges"Constant
employee_id has unique valuesUnique
privilege_id has unique valuesUnique
__table_name__ has unique valuesUnique

Reproduction
Analysis started2025-10-30 16:47:06.470150
Analysis finished2025-10-30 16:47:06.519499
Duration0.05 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

employee_id
Real number (ℝ)

Constant  Unique 

Distinct1
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2
Minimum2
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size137.0 B
2025-10-30T18:47:06.532918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum2
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum2
Range0
Interquartile range (IQR)0

Descriptive statistics
2025-10-30T18:47:06.610292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
Standard deviation
Coefficient of variation (CV)
Kurtosis
Mean2
Median Absolute Deviation (MAD)0
Skewness
Sum2
Variance
MonotonicityStrictly increasing
ValueCountFrequency (%)
21
100.0%

ValueCountFrequency (%)
21
100.0%
ValueCountFrequency (%)
21
100.0%

privilege_id
Real number (ℝ)

Constant  Unique 

Distinct1
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2
Minimum2
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size137.0 B
2025-10-30T18:47:06.663463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum2
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum2
Range0
Interquartile range (IQR)0

Descriptive statistics
2025-10-30T18:47:06.714682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
Standard deviation
Coefficient of variation (CV)
Kurtosis
Mean2
Median Absolute Deviation (MAD)0
Skewness
Sum2
Variance
MonotonicityStrictly increasing
ValueCountFrequency (%)
21
100.0%

ValueCountFrequency (%)
21
100.0%
ValueCountFrequency (%)
21
100.0%

__table_name__
Text

Constant  Unique 

Distinct1
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size204.0 B
2025-10-30T18:47:06.773896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length19
Median length19
Mean length19
Min length19

Characters and Unicode
Total characters19
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique1 ?
Unique (%)100.0%

Sample
1st rowemployee_privileges

ValueCountFrequency (%)
employee_privileges1
100.0%
2025-10-30T18:47:06.899763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e5
26.3%
p2
 
10.5%
l2
 
10.5%
i2
 
10.5%
m1
 
5.3%
o1
 
5.3%
_1
 
5.3%
y1
 
5.3%
r1
 
5.3%
v1
 
5.3%
Other values (2)2
 
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e5
26.3%
p2
 
10.5%
l2
 
10.5%
i2
 
10.5%
m1
 
5.3%
o1
 
5.3%
_1
 
5.3%
y1
 
5.3%
r1
 
5.3%
v1
 
5.3%
Other values (2)2
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e5
26.3%
p2
 
10.5%
l2
 
10.5%
i2
 
10.5%
m1
 
5.3%
o1
 
5.3%
_1
 
5.3%
y1
 
5.3%
r1
 
5.3%
v1
 
5.3%
Other values (2)2
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e5
26.3%
p2
 
10.5%
l2
 
10.5%
i2
 
10.5%
m1
 
5.3%
o1
 
5.3%
_1
 
5.3%
y1
 
5.3%
r1
 
5.3%
v1
 
5.3%
Other values (2)2
 
10.5%

Report generated by YData.


Table: employees

Profile: employees

Overview

Brought to you by YData

Dataset statistics
Number of variables19
Number of observations9
Missing cells12
Missing cells (%)7.0%
Total size in memory11.1 KiB
Average record size in memory1.2 KiB

Variable types
Numeric1
Text17
Unsupported1

Alerts

company has constant value "Northwind Traders"Constant
business_phone has constant value "(123)555-0100"Constant
home_phone has constant value "(123)555-0102"Constant
fax_number has constant value "(123)555-0103"Constant
state_province has constant value "WA"Constant
zip_postal_code has constant value "99999"Constant
country_region has constant value "USA"Constant
attachments has constant value ""Constant
__table_name__ has constant value "employees"Constant
mobile_phone has 9 (100.0%) missing valuesMissing
notes has 3 (33.3%) missing valuesMissing
id has unique valuesUnique
last_name has unique valuesUnique
first_name has unique valuesUnique
email_address has unique valuesUnique
address has unique valuesUnique
mobile_phone is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction
Analysis started2025-10-30 16:47:07.629091
Analysis finished2025-10-30 16:47:07.815765
Duration0.19 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size209.0 B
2025-10-30T18:47:07.870369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum1
5-th percentile1.4
Q13
median5
Q37
95-th percentile8.6
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics
Standard deviation2.738612788
Coefficient of variation (CV)0.5477225575
Kurtosis-1.2
Mean5
Median Absolute Deviation (MAD)2
Skewness0
Sum45
Variance7.5
MonotonicityNot monotonic

2025-10-30T18:47:07.949667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
81
11.1%
51
11.1%
71
11.1%
11
11.1%
61
11.1%
31
11.1%
41
11.1%
21
11.1%
91
11.1%
ValueCountFrequency (%)
11
11.1%
21
11.1%
31
11.1%
41
11.1%
51
11.1%
ValueCountFrequency (%)
91
11.1%
81
11.1%
71
11.1%
61
11.1%
51
11.1%

company
Text

Constant 

Distinct1
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size794.0 B
2025-10-30T18:47:08.010966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length17
Median length17
Mean length17
Min length17

Characters and Unicode
Total characters153
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowNorthwind Traders
2nd rowNorthwind Traders
3rd rowNorthwind Traders
4th rowNorthwind Traders
5th rowNorthwind Traders

ValueCountFrequency (%)
northwind9
50.0%
traders9
50.0%
2025-10-30T18:47:08.126979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r27
17.6%
d18
11.8%
o9
 
5.9%
t9
 
5.9%
h9
 
5.9%
N9
 
5.9%
w9
 
5.9%
i9
 
5.9%
n9
 
5.9%
9
 
5.9%
Other values (4)36
23.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)153
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r27
17.6%
d18
11.8%
o9
 
5.9%
t9
 
5.9%
h9
 
5.9%
N9
 
5.9%
w9
 
5.9%
i9
 
5.9%
n9
 
5.9%
9
 
5.9%
Other values (4)36
23.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)153
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r27
17.6%
d18
11.8%
o9
 
5.9%
t9
 
5.9%
h9
 
5.9%
N9
 
5.9%
w9
 
5.9%
i9
 
5.9%
n9
 
5.9%
9
 
5.9%
Other values (4)36
23.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)153
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r27
17.6%
d18
11.8%
o9
 
5.9%
t9
 
5.9%
h9
 
5.9%
N9
 
5.9%
w9
 
5.9%
i9
 
5.9%
n9
 
5.9%
9
 
5.9%
Other values (4)36
23.5%

last_name
Text

Unique 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size710.0 B
2025-10-30T18:47:08.187329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length14
Median length8
Mean length7.666666667
Min length4

Characters and Unicode
Total characters69
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique9 ?
Unique (%)100.0%

Sample
1st rowGiussani
2nd rowThorpe
3rd rowZare
4th rowFreehafer
5th rowNeipper

ValueCountFrequency (%)
giussani1
11.1%
thorpe1
11.1%
zare1
11.1%
freehafer1
11.1%
neipper1
11.1%
kotas1
11.1%
sergienko1
11.1%
cencini1
11.1%
hellung-larsen1
11.1%
2025-10-30T18:47:08.325871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e12
17.4%
r7
 
10.1%
n6
 
8.7%
i6
 
8.7%
a5
 
7.2%
s4
 
5.8%
p3
 
4.3%
o3
 
4.3%
h2
 
2.9%
g2
 
2.9%
Other values (17)19
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)69
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e12
17.4%
r7
 
10.1%
n6
 
8.7%
i6
 
8.7%
a5
 
7.2%
s4
 
5.8%
p3
 
4.3%
o3
 
4.3%
h2
 
2.9%
g2
 
2.9%
Other values (17)19
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)69
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e12
17.4%
r7
 
10.1%
n6
 
8.7%
i6
 
8.7%
a5
 
7.2%
s4
 
5.8%
p3
 
4.3%
o3
 
4.3%
h2
 
2.9%
g2
 
2.9%
Other values (17)19
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)69
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e12
17.4%
r7
 
10.1%
n6
 
8.7%
i6
 
8.7%
a5
 
7.2%
s4
 
5.8%
p3
 
4.3%
o3
 
4.3%
h2
 
2.9%
g2
 
2.9%
Other values (17)19
27.5%

first_name
Text

Unique 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size689.0 B
2025-10-30T18:47:08.391937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length7
Median length6
Mean length5.333333333
Min length3

Characters and Unicode
Total characters48
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique9 ?
Unique (%)100.0%

Sample
1st rowLaura
2nd rowSteven
3rd rowRobert
4th rowNancy
5th rowMichael

ValueCountFrequency (%)
laura1
11.1%
steven1
11.1%
robert1
11.1%
nancy1
11.1%
michael1
11.1%
jan1
11.1%
mariya1
11.1%
andrew1
11.1%
anne1
11.1%
2025-10-30T18:47:08.535685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a7
14.6%
e6
12.5%
n6
12.5%
r4
 
8.3%
t2
 
4.2%
y2
 
4.2%
c2
 
4.2%
i2
 
4.2%
A2
 
4.2%
M2
 
4.2%
Other values (13)13
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)48
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a7
14.6%
e6
12.5%
n6
12.5%
r4
 
8.3%
t2
 
4.2%
y2
 
4.2%
c2
 
4.2%
i2
 
4.2%
A2
 
4.2%
M2
 
4.2%
Other values (13)13
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)48
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a7
14.6%
e6
12.5%
n6
12.5%
r4
 
8.3%
t2
 
4.2%
y2
 
4.2%
c2
 
4.2%
i2
 
4.2%
A2
 
4.2%
M2
 
4.2%
Other values (13)13
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)48
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a7
14.6%
e6
12.5%
n6
12.5%
r4
 
8.3%
t2
 
4.2%
y2
 
4.2%
c2
 
4.2%
i2
 
4.2%
A2
 
4.2%
M2
 
4.2%
Other values (13)13
27.1%

email_address
Text

Unique 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size878.0 B
2025-10-30T18:47:08.606763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length28
Median length27
Mean length26.33333333
Min length24

Characters and Unicode
Total characters237
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique9 ?
Unique (%)100.0%

Sample
1st rowlaura@northwindtraders.com
2nd rowsteven@northwindtraders.com
3rd rowrobert@northwindtraders.com
4th rownancy@northwindtraders.com
5th rowmichael@northwindtraders.com

ValueCountFrequency (%)
laura@northwindtraders.com1
11.1%
steven@northwindtraders.com1
11.1%
robert@northwindtraders.com1
11.1%
nancy@northwindtraders.com1
11.1%
michael@northwindtraders.com1
11.1%
jan@northwindtraders.com1
11.1%
mariya@northwindtraders.com1
11.1%
andrew@northwindtraders.com1
11.1%
anne@northwindtraders.com1
11.1%
2025-10-30T18:47:08.784753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r32
13.5%
n25
10.5%
t20
 
8.4%
d19
 
8.0%
o19
 
8.0%
a18
 
7.6%
e15
 
6.3%
c11
 
4.6%
i11
 
4.6%
m11
 
4.6%
Other values (11)56
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)237
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r32
13.5%
n25
10.5%
t20
 
8.4%
d19
 
8.0%
o19
 
8.0%
a18
 
7.6%
e15
 
6.3%
c11
 
4.6%
i11
 
4.6%
m11
 
4.6%
Other values (11)56
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)237
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r32
13.5%
n25
10.5%
t20
 
8.4%
d19
 
8.0%
o19
 
8.0%
a18
 
7.6%
e15
 
6.3%
c11
 
4.6%
i11
 
4.6%
m11
 
4.6%
Other values (11)56
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)237
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r32
13.5%
n25
10.5%
t20
 
8.4%
d19
 
8.0%
o19
 
8.0%
a18
 
7.6%
e15
 
6.3%
c11
 
4.6%
i11
 
4.6%
m11
 
4.6%
Other values (11)56
23.6%
Distinct4
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Memory size812.0 B
2025-10-30T18:47:08.844139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length21
Median length20
Mean length19
Min length13

Characters and Unicode
Total characters171
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique3 ?
Unique (%)33.3%

Sample
1st rowSales Coordinator
2nd rowSales Manager
3rd rowSales Representative
4th rowSales Representative
5th rowSales Representative

ValueCountFrequency (%)
sales9
47.4%
representative6
31.6%
coordinator1
 
5.3%
manager1
 
5.3%
vice1
 
5.3%
president1
 
5.3%
2025-10-30T18:47:09.077129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e37
21.6%
a18
10.5%
s16
9.4%
t14
 
8.2%
r10
 
5.8%
10
 
5.8%
l9
 
5.3%
S9
 
5.3%
i9
 
5.3%
n9
 
5.3%
Other values (12)30
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)171
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e37
21.6%
a18
10.5%
s16
9.4%
t14
 
8.2%
r10
 
5.8%
10
 
5.8%
l9
 
5.3%
S9
 
5.3%
i9
 
5.3%
n9
 
5.3%
Other values (12)30
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)171
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e37
21.6%
a18
10.5%
s16
9.4%
t14
 
8.2%
r10
 
5.8%
10
 
5.8%
l9
 
5.3%
S9
 
5.3%
i9
 
5.3%
n9
 
5.3%
Other values (12)30
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)171
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e37
21.6%
a18
10.5%
s16
9.4%
t14
 
8.2%
r10
 
5.8%
10
 
5.8%
l9
 
5.3%
S9
 
5.3%
i9
 
5.3%
n9
 
5.3%
Other values (12)30
17.5%

business_phone
Text

Constant 

Distinct1
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size758.0 B
2025-10-30T18:47:09.127611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length13
Median length13
Mean length13
Min length13

Characters and Unicode
Total characters117
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st row(123)555-0100
2nd row(123)555-0100
3rd row(123)555-0100
4th row(123)555-0100
5th row(123)555-0100

ValueCountFrequency (%)
123)555-01009
100.0%
2025-10-30T18:47:09.239467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
027
23.1%
527
23.1%
118
15.4%
(9
 
7.7%
39
 
7.7%
29
 
7.7%
)9
 
7.7%
-9
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)117
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
027
23.1%
527
23.1%
118
15.4%
(9
 
7.7%
39
 
7.7%
29
 
7.7%
)9
 
7.7%
-9
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)117
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
027
23.1%
527
23.1%
118
15.4%
(9
 
7.7%
39
 
7.7%
29
 
7.7%
)9
 
7.7%
-9
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)117
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
027
23.1%
527
23.1%
118
15.4%
(9
 
7.7%
39
 
7.7%
29
 
7.7%
)9
 
7.7%
-9
 
7.7%

home_phone
Text

Constant 

Distinct1
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size758.0 B
2025-10-30T18:47:09.291191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length13
Median length13
Mean length13
Min length13

Characters and Unicode
Total characters117
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st row(123)555-0102
2nd row(123)555-0102
3rd row(123)555-0102
4th row(123)555-0102
5th row(123)555-0102

ValueCountFrequency (%)
123)555-01029
100.0%
2025-10-30T18:47:09.394818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
527
23.1%
118
15.4%
018
15.4%
218
15.4%
39
 
7.7%
(9
 
7.7%
)9
 
7.7%
-9
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)117
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
527
23.1%
118
15.4%
018
15.4%
218
15.4%
39
 
7.7%
(9
 
7.7%
)9
 
7.7%
-9
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)117
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
527
23.1%
118
15.4%
018
15.4%
218
15.4%
39
 
7.7%
(9
 
7.7%
)9
 
7.7%
-9
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)117
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
527
23.1%
118
15.4%
018
15.4%
218
15.4%
39
 
7.7%
(9
 
7.7%
)9
 
7.7%
-9
 
7.7%

mobile_phone
Unsupported

Missing  Rejected  Unsupported 

Missing9
Missing (%)100.0%
Memory size200.0 B

fax_number
Text

Constant 

Distinct1
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size758.0 B
2025-10-30T18:47:09.446409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length13
Median length13
Mean length13
Min length13

Characters and Unicode
Total characters117
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st row(123)555-0103
2nd row(123)555-0103
3rd row(123)555-0103
4th row(123)555-0103
5th row(123)555-0103

ValueCountFrequency (%)
123)555-01039
100.0%
2025-10-30T18:47:09.557871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
527
23.1%
118
15.4%
018
15.4%
318
15.4%
29
 
7.7%
(9
 
7.7%
)9
 
7.7%
-9
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)117
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
527
23.1%
118
15.4%
018
15.4%
318
15.4%
29
 
7.7%
(9
 
7.7%
)9
 
7.7%
-9
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)117
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
527
23.1%
118
15.4%
018
15.4%
318
15.4%
29
 
7.7%
(9
 
7.7%
)9
 
7.7%
-9
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)117
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
527
23.1%
118
15.4%
018
15.4%
318
15.4%
29
 
7.7%
(9
 
7.7%
)9
 
7.7%
-9
 
7.7%

address
Text

Unique 

Distinct9
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size767.0 B
2025-10-30T18:47:09.623542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length14
Median length14
Mean length14
Min length14

Characters and Unicode
Total characters126
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique9 ?
Unique (%)100.0%

Sample
1st row123 8th Avenue
2nd row123 5th Avenue
3rd row123 7th Avenue
4th row123 1st Avenue
5th row123 6th Avenue

ValueCountFrequency (%)
1239
33.3%
avenue9
33.3%
8th1
 
3.7%
5th1
 
3.7%
7th1
 
3.7%
1st1
 
3.7%
6th1
 
3.7%
3rd1
 
3.7%
4th1
 
3.7%
2nd1
 
3.7%
2025-10-30T18:47:09.775306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18
14.3%
e18
14.3%
210
7.9%
110
7.9%
310
7.9%
n10
7.9%
A9
7.1%
v9
7.1%
u9
7.1%
t7
 
5.6%
Other values (10)16
12.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)126
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18
14.3%
e18
14.3%
210
7.9%
110
7.9%
310
7.9%
n10
7.9%
A9
7.1%
v9
7.1%
u9
7.1%
t7
 
5.6%
Other values (10)16
12.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)126
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18
14.3%
e18
14.3%
210
7.9%
110
7.9%
310
7.9%
n10
7.9%
A9
7.1%
v9
7.1%
u9
7.1%
t7
 
5.6%
Other values (10)16
12.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)126
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18
14.3%
e18
14.3%
210
7.9%
110
7.9%
310
7.9%
n10
7.9%
A9
7.1%
v9
7.1%
u9
7.1%
t7
 
5.6%
Other values (10)16
12.7%

city
Text

Distinct4
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Memory size706.0 B
2025-10-30T18:47:09.845459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length8
Median length7
Mean length7.222222222
Min length7

Characters and Unicode
Total characters65
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique2 ?
Unique (%)22.2%

Sample
1st rowRedmond
2nd rowSeattle
3rd rowSeattle
4th rowSeattle
5th rowRedmond

ValueCountFrequency (%)
seattle4
44.4%
redmond3
33.3%
kirkland1
 
11.1%
bellevue1
 
11.1%
2025-10-30T18:47:09.984328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e14
21.5%
t8
12.3%
l7
10.8%
d7
10.8%
a5
 
7.7%
S4
 
6.2%
n4
 
6.2%
m3
 
4.6%
R3
 
4.6%
o3
 
4.6%
Other values (7)7
10.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)65
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e14
21.5%
t8
12.3%
l7
10.8%
d7
10.8%
a5
 
7.7%
S4
 
6.2%
n4
 
6.2%
m3
 
4.6%
R3
 
4.6%
o3
 
4.6%
Other values (7)7
10.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)65
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e14
21.5%
t8
12.3%
l7
10.8%
d7
10.8%
a5
 
7.7%
S4
 
6.2%
n4
 
6.2%
m3
 
4.6%
R3
 
4.6%
o3
 
4.6%
Other values (7)7
10.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)65
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e14
21.5%
t8
12.3%
l7
10.8%
d7
10.8%
a5
 
7.7%
S4
 
6.2%
n4
 
6.2%
m3
 
4.6%
R3
 
4.6%
o3
 
4.6%
Other values (7)7
10.8%

state_province
Text

Constant 

Distinct1
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size659.0 B
2025-10-30T18:47:10.019694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length2
Median length2
Mean length2
Min length2

Characters and Unicode
Total characters18
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowWA
2nd rowWA
3rd rowWA
4th rowWA
5th rowWA

ValueCountFrequency (%)
wa9
100.0%
2025-10-30T18:47:10.116400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
W9
50.0%
A9
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)18
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W9
50.0%
A9
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W9
50.0%
A9
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W9
50.0%
A9
50.0%

zip_postal_code
Text

Constant 

Distinct1
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size686.0 B
2025-10-30T18:47:10.159422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length5
Median length5
Mean length5
Min length5

Characters and Unicode
Total characters45
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st row99999
2nd row99999
3rd row99999
4th row99999
5th row99999

ValueCountFrequency (%)
999999
100.0%
2025-10-30T18:47:10.259931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
945
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)45
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
945
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
945
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
945
100.0%

country_region
Text

Constant 

Distinct1
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size668.0 B
2025-10-30T18:47:10.300576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length3
Median length3
Mean length3
Min length3

Characters and Unicode
Total characters27
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowUSA
2nd rowUSA
3rd rowUSA
4th rowUSA
5th rowUSA

ValueCountFrequency (%)
usa9
100.0%
2025-10-30T18:47:10.394175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U9
33.3%
S9
33.3%
A9
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)27
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U9
33.3%
S9
33.3%
A9
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)27
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U9
33.3%
S9
33.3%
A9
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)27
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U9
33.3%
S9
33.3%
A9
33.3%
Distinct2
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2025-10-30T18:47:10.482289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length57
Median length57
Mean length53.88888889
Min length29

Characters and Unicode
Total characters485
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique1 ?
Unique (%)11.1%

Sample
1st rowhttp://northwindtraders.com#http://northwindtraders.com/#
2nd rowhttp://northwindtraders.com#http://northwindtraders.com/#
3rd rowhttp://northwindtraders.com#http://northwindtraders.com/#
4th row#http://northwindtraders.com#
5th rowhttp://northwindtraders.com#http://northwindtraders.com/#

ValueCountFrequency (%)
http://northwindtraders.com#http://northwindtraders.com8
88.9%
http://northwindtraders.com1
 
11.1%
2025-10-30T18:47:10.620370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t68
14.0%
r51
 
10.5%
/42
 
8.7%
h34
 
7.0%
o34
 
7.0%
n34
 
7.0%
d34
 
7.0%
#18
 
3.7%
p17
 
3.5%
:17
 
3.5%
Other values (8)136
28.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)485
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t68
14.0%
r51
 
10.5%
/42
 
8.7%
h34
 
7.0%
o34
 
7.0%
n34
 
7.0%
d34
 
7.0%
#18
 
3.7%
p17
 
3.5%
:17
 
3.5%
Other values (8)136
28.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)485
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t68
14.0%
r51
 
10.5%
/42
 
8.7%
h34
 
7.0%
o34
 
7.0%
n34
 
7.0%
d34
 
7.0%
#18
 
3.7%
p17
 
3.5%
:17
 
3.5%
Other values (8)136
28.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)485
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t68
14.0%
r51
 
10.5%
/42
 
8.7%
h34
 
7.0%
o34
 
7.0%
n34
 
7.0%
d34
 
7.0%
#18
 
3.7%
p17
 
3.5%
:17
 
3.5%
Other values (8)136
28.0%

notes
Text

Missing 

Distinct6
Distinct (%)100.0%
Missing3
Missing (%)33.3%
Memory size981.0 B
2025-10-30T18:47:10.692079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length119
Median length73
Mean length69.16666667
Min length24

Characters and Unicode
Total characters415
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique6 ?
Unique (%)100.0%

Sample
1st rowReads and writes French.
2nd rowJoined the company as a sales representative and was promoted to sales manager. Fluent in French.
3rd rowFluent in Japanese and can read and write French, Portuguese, and Spanish.
4th rowWas hired as a sales associate and was promoted to sales representative.
5th rowJoined the company as a sales representative, was promoted to sales manager and was then named vice president of sales.

ValueCountFrequency (%)
and8
 
11.6%
sales7
 
10.1%
was5
 
7.2%
french4
 
5.8%
a3
 
4.3%
as3
 
4.3%
representative3
 
4.3%
to3
 
4.3%
promoted3
 
4.3%
in3
 
4.3%
Other values (21)27
39.1%
2025-10-30T18:47:10.843637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
64
15.4%
e51
12.3%
a45
10.8%
n34
 
8.2%
s33
 
8.0%
t23
 
5.5%
r22
 
5.3%
d18
 
4.3%
o16
 
3.9%
i15
 
3.6%
Other values (20)94
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)415
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
64
15.4%
e51
12.3%
a45
10.8%
n34
 
8.2%
s33
 
8.0%
t23
 
5.5%
r22
 
5.3%
d18
 
4.3%
o16
 
3.9%
i15
 
3.6%
Other values (20)94
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)415
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
64
15.4%
e51
12.3%
a45
10.8%
n34
 
8.2%
s33
 
8.0%
t23
 
5.5%
r22
 
5.3%
d18
 
4.3%
o16
 
3.9%
i15
 
3.6%
Other values (20)94
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)415
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
64
15.4%
e51
12.3%
a45
10.8%
n34
 
8.2%
s33
 
8.0%
t23
 
5.5%
r22
 
5.3%
d18
 
4.3%
o16
 
3.9%
i15
 
3.6%
Other values (20)94
22.7%

attachments
Text

Constant 

Distinct1
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size641.0 B

Length
Max length0
Median length0
Mean length0
Min length0

Characters and Unicode
Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st row
2nd row
3rd row
4th row
5th row

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

__table_name__
Text

Constant 

Distinct1
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size722.0 B
2025-10-30T18:47:10.977771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length9
Median length9
Mean length9
Min length9

Characters and Unicode
Total characters81
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowemployees
2nd rowemployees
3rd rowemployees
4th rowemployees
5th rowemployees

ValueCountFrequency (%)
employees9
100.0%
2025-10-30T18:47:11.080987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e27
33.3%
m9
 
11.1%
p9
 
11.1%
l9
 
11.1%
o9
 
11.1%
y9
 
11.1%
s9
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)81
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e27
33.3%
m9
 
11.1%
p9
 
11.1%
l9
 
11.1%
o9
 
11.1%
y9
 
11.1%
s9
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)81
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e27
33.3%
m9
 
11.1%
p9
 
11.1%
l9
 
11.1%
o9
 
11.1%
y9
 
11.1%
s9
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)81
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e27
33.3%
m9
 
11.1%
p9
 
11.1%
l9
 
11.1%
o9
 
11.1%
y9
 
11.1%
s9
 
11.1%

Report generated by YData.


Table: inventory_transaction_types

Profile: inventory_transaction_types

Overview

Brought to you by YData

Dataset statistics
Number of variables3
Number of observations4
Missing cells0
Missing cells (%)0.0%
Total size in memory753.0 B
Average record size in memory188.2 B

Variable types
Numeric1
Text2

Alerts

__table_name__ has constant value "inventory_transaction_types"Constant
id has unique valuesUnique
type_name has unique valuesUnique

Reproduction
Analysis started2025-10-30 16:47:12.185719
Analysis finished2025-10-30 16:47:12.221050
Duration0.04 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct4
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size164.0 B
2025-10-30T18:47:12.261427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum1
5-th percentile1.15
Q11.75
median2.5
Q33.25
95-th percentile3.85
Maximum4
Range3
Interquartile range (IQR)1.5

Descriptive statistics
Standard deviation1.290994449
Coefficient of variation (CV)0.5163977795
Kurtosis-1.2
Mean2.5
Median Absolute Deviation (MAD)1
Skewness0
Sum10
Variance1.666666667
MonotonicityStrictly increasing

2025-10-30T18:47:12.337491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
11
25.0%
21
25.0%
31
25.0%
41
25.0%
ValueCountFrequency (%)
11
25.0%
21
25.0%
31
25.0%
41
25.0%
ValueCountFrequency (%)
41
25.0%
31
25.0%
21
25.0%
11
25.0%

type_name
Text

Unique 

Distinct4
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size381.0 B
2025-10-30T18:47:12.401985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length9
Median length6
Mean length6.25
Min length4

Characters and Unicode
Total characters25
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique4 ?
Unique (%)100.0%

Sample
1st rowPurchased
2nd rowSold
3rd rowOn Hold
4th rowWaste

ValueCountFrequency (%)
purchased1
20.0%
sold1
20.0%
on1
20.0%
hold1
20.0%
waste1
20.0%
2025-10-30T18:47:12.515731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d3
 
12.0%
a2
 
8.0%
l2
 
8.0%
o2
 
8.0%
s2
 
8.0%
e2
 
8.0%
u1
 
4.0%
P1
 
4.0%
h1
 
4.0%
c1
 
4.0%
Other values (8)8
32.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)25
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d3
 
12.0%
a2
 
8.0%
l2
 
8.0%
o2
 
8.0%
s2
 
8.0%
e2
 
8.0%
u1
 
4.0%
P1
 
4.0%
h1
 
4.0%
c1
 
4.0%
Other values (8)8
32.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)25
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d3
 
12.0%
a2
 
8.0%
l2
 
8.0%
o2
 
8.0%
s2
 
8.0%
e2
 
8.0%
u1
 
4.0%
P1
 
4.0%
h1
 
4.0%
c1
 
4.0%
Other values (8)8
32.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)25
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d3
 
12.0%
a2
 
8.0%
l2
 
8.0%
o2
 
8.0%
s2
 
8.0%
e2
 
8.0%
u1
 
4.0%
P1
 
4.0%
h1
 
4.0%
c1
 
4.0%
Other values (8)8
32.0%

__table_name__
Text

Constant 

Distinct1
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size464.0 B
2025-10-30T18:47:12.581024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length27
Median length27
Mean length27
Min length27

Characters and Unicode
Total characters108
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowinventory_transaction_types
2nd rowinventory_transaction_types
3rd rowinventory_transaction_types
4th rowinventory_transaction_types

ValueCountFrequency (%)
inventory_transaction_types4
100.0%
2025-10-30T18:47:12.706232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n16
14.8%
t16
14.8%
i8
7.4%
e8
7.4%
o8
7.4%
a8
7.4%
r8
7.4%
y8
7.4%
_8
7.4%
s8
7.4%
Other values (3)12
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)108
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n16
14.8%
t16
14.8%
i8
7.4%
e8
7.4%
o8
7.4%
a8
7.4%
r8
7.4%
y8
7.4%
_8
7.4%
s8
7.4%
Other values (3)12
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)108
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n16
14.8%
t16
14.8%
i8
7.4%
e8
7.4%
o8
7.4%
a8
7.4%
r8
7.4%
y8
7.4%
_8
7.4%
s8
7.4%
Other values (3)12
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)108
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n16
14.8%
t16
14.8%
i8
7.4%
e8
7.4%
o8
7.4%
a8
7.4%
r8
7.4%
y8
7.4%
_8
7.4%
s8
7.4%
Other values (3)12
11.1%

Report generated by YData.


Table: inventory_transactions

Profile: inventory_transactions

Overview

Brought to you by YData

Dataset statistics
Number of variables10
Number of observations102
Missing cells292
Missing cells (%)28.6%
Total size in memory18.3 KiB
Average record size in memory183.7 B

Variable types
Numeric4
DateTime2
Unsupported2
Text2

Alerts

__table_name__ has constant value "inventory_transactions"Constant
purchase_order_id has 102 (100.0%) missing valuesMissing
customer_order_id has 102 (100.0%) missing valuesMissing
comments has 88 (86.3%) missing valuesMissing
id has unique valuesUnique
transaction_created_date has unique valuesUnique
purchase_order_id is an unsupported type, check if it needs cleaning or further analysisUnsupported
customer_order_id is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction
Analysis started2025-10-30 16:47:13.500625
Analysis finished2025-10-30 16:47:13.574116
Duration0.07 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct102
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.5
Minimum35
Maximum136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2025-10-30T18:47:13.637328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum35
5-th percentile40.05
Q160.25
median85.5
Q3110.75
95-th percentile130.95
Maximum136
Range101
Interquartile range (IQR)50.5

Descriptive statistics
Standard deviation29.58884925
Coefficient of variation (CV)0.3460684123
Kurtosis-1.2
Mean85.5
Median Absolute Deviation (MAD)25.5
Skewness0
Sum8721
Variance875.5
MonotonicityNot monotonic

2025-10-30T18:47:13.748665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1111
 
1.0%
451
 
1.0%
471
 
1.0%
521
 
1.0%
1051
 
1.0%
1091
 
1.0%
801
 
1.0%
361
 
1.0%
411
 
1.0%
421
 
1.0%
Other values (92)92
90.2%
ValueCountFrequency (%)
351
1.0%
361
1.0%
371
1.0%
381
1.0%
391
1.0%
ValueCountFrequency (%)
1361
1.0%
1351
1.0%
1341
1.0%
1331
1.0%
1321
1.0%

transaction_type
Real number (ℝ)

Distinct3
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.676470588
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2025-10-30T18:47:13.830483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)1

Descriptive statistics
Standard deviation0.6473367328
Coefficient of variation (CV)0.3861306827
Kurtosis-0.6842029788
Mean1.676470588
Median Absolute Deviation (MAD)1
Skewness0.4293638873
Sum171
Variance0.4190448457
MonotonicityIncreasing

2025-10-30T18:47:13.894533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
249
48.0%
143
42.2%
310
 
9.8%
ValueCountFrequency (%)
143
42.2%
249
48.0%
310
 
9.8%
ValueCountFrequency (%)
310
 
9.8%
249
48.0%
143
42.2%
Distinct102
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size944.0 B
Minimum2006-03-22 16:02:28
Maximum2006-04-25 17:04:05
Invalid dates0
Invalid dates (%)0.0%
2025-10-30T18:47:14.149632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:47:14.254549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct88
Distinct (%)86.3%
Missing0
Missing (%)0.0%
Memory size944.0 B
Minimum2006-03-22 16:02:28
Maximum2006-04-25 17:04:57
Invalid dates0
Invalid dates (%)0.0%
2025-10-30T18:47:14.369600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:47:14.467167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

product_id
Real number (ℝ)

Distinct28
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.80392157
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2025-10-30T18:47:14.549779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum1
5-th percentile4
Q119
median40.5
Q351.75
95-th percentile80
Maximum81
Range80
Interquartile range (IQR)32.75

Descriptive statistics
Standard deviation24.61788741
Coefficient of variation (CV)0.6511993039
Kurtosis-0.9794910262
Mean37.80392157
Median Absolute Deviation (MAD)21.5
Skewness0.2176139641
Sum3856
Variance606.0403805
MonotonicityNot monotonic

2025-10-30T18:47:14.630255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
3410
 
9.8%
198
 
7.8%
438
 
7.8%
417
 
6.9%
487
 
6.9%
86
 
5.9%
805
 
4.9%
815
 
4.9%
404
 
3.9%
724
 
3.9%
Other values (18)38
37.3%
ValueCountFrequency (%)
13
2.9%
32
2.0%
43
2.9%
52
2.0%
63
2.9%
ValueCountFrequency (%)
815
4.9%
805
4.9%
771
 
1.0%
742
 
2.0%
724
3.9%

quantity
Real number (ℝ)

Distinct24
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.85294118
Minimum1
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2025-10-30T18:47:14.701642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum1
5-th percentile10
Q120
median40
Q389.25
95-th percentile247.5
Maximum300
Range299
Interquartile range (IQR)69.25

Descriptive statistics
Standard deviation71.72104888
Coefficient of variation (CV)1.105902795
Kurtosis4.094259858
Mean64.85294118
Median Absolute Deviation (MAD)20
Skewness2.118255623
Sum6615
Variance5143.908853
MonotonicityNot monotonic

2025-10-30T18:47:14.779685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4019
18.6%
1011
10.8%
10011
10.8%
209
8.8%
308
7.8%
508
7.8%
258
7.8%
3005
 
4.9%
2004
 
3.9%
602
 
2.0%
Other values (14)17
16.7%
ValueCountFrequency (%)
11
 
1.0%
31
 
1.0%
51
 
1.0%
1011
10.8%
121
 
1.0%
ValueCountFrequency (%)
3005
4.9%
2501
 
1.0%
2004
3.9%
1251
 
1.0%
1202
 
2.0%

purchase_order_id
Unsupported

Missing  Rejected  Unsupported 

Missing102
Missing (%)100.0%
Memory size1.0 KiB

customer_order_id
Unsupported

Missing  Rejected  Unsupported 

Missing102
Missing (%)100.0%
Memory size1.0 KiB

comments
Text

Missing 

Distinct12
Distinct (%)85.7%
Missing88
Missing (%)86.3%
Memory size4.1 KiB
2025-10-30T18:47:14.861407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length36
Median length36
Mean length36
Min length36

Characters and Unicode
Total characters504
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique10 ?
Unique (%)71.4%

Sample
1st rowFill Back Ordered product, Order #42
2nd rowFill Back Ordered product, Order #48
3rd rowFill Back Ordered product, Order #48
4th rowFill Back Ordered product, Order #33
5th rowFill Back Ordered product, Order #46

ValueCountFrequency (%)
fill14
16.7%
back14
16.7%
ordered14
16.7%
product14
16.7%
order14
16.7%
482
 
2.4%
462
 
2.4%
421
 
1.2%
331
 
1.2%
451
 
1.2%
Other values (7)7
8.3%
2025-10-30T18:47:14.996467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
70
13.9%
r70
13.9%
d56
 
11.1%
e42
 
8.3%
O28
 
5.6%
c28
 
5.6%
l28
 
5.6%
i14
 
2.8%
F14
 
2.8%
k14
 
2.8%
Other values (18)140
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
70
13.9%
r70
13.9%
d56
 
11.1%
e42
 
8.3%
O28
 
5.6%
c28
 
5.6%
l28
 
5.6%
i14
 
2.8%
F14
 
2.8%
k14
 
2.8%
Other values (18)140
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
70
13.9%
r70
13.9%
d56
 
11.1%
e42
 
8.3%
O28
 
5.6%
c28
 
5.6%
l28
 
5.6%
i14
 
2.8%
F14
 
2.8%
k14
 
2.8%
Other values (18)140
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
70
13.9%
r70
13.9%
d56
 
11.1%
e42
 
8.3%
O28
 
5.6%
c28
 
5.6%
l28
 
5.6%
i14
 
2.8%
F14
 
2.8%
k14
 
2.8%
Other values (18)140
27.8%

__table_name__
Text

Constant 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size8.0 KiB
2025-10-30T18:47:15.056603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length22
Median length22
Mean length22
Min length22

Characters and Unicode
Total characters2244
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowinventory_transactions
2nd rowinventory_transactions
3rd rowinventory_transactions
4th rowinventory_transactions
5th rowinventory_transactions

ValueCountFrequency (%)
inventory_transactions102
100.0%
2025-10-30T18:47:15.174311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n408
18.2%
t306
13.6%
o204
9.1%
i204
9.1%
s204
9.1%
a204
9.1%
r204
9.1%
v102
 
4.5%
e102
 
4.5%
y102
 
4.5%
Other values (2)204
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2244
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n408
18.2%
t306
13.6%
o204
9.1%
i204
9.1%
s204
9.1%
a204
9.1%
r204
9.1%
v102
 
4.5%
e102
 
4.5%
y102
 
4.5%
Other values (2)204
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2244
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n408
18.2%
t306
13.6%
o204
9.1%
i204
9.1%
s204
9.1%
a204
9.1%
r204
9.1%
v102
 
4.5%
e102
 
4.5%
y102
 
4.5%
Other values (2)204
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2244
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n408
18.2%
t306
13.6%
o204
9.1%
i204
9.1%
s204
9.1%
a204
9.1%
r204
9.1%
v102
 
4.5%
e102
 
4.5%
y102
 
4.5%
Other values (2)204
9.1%

Report generated by YData.


Table: invoices

Profile: invoices

Overview

Brought to you by YData

Dataset statistics
Number of variables8
Number of observations35
Missing cells35
Missing cells (%)12.5%
Total size in memory4.3 KiB
Average record size in memory126.7 B

Variable types
Numeric5
DateTime1
Unsupported1
Text1

Alerts

tax has constant value "0.0"Constant
shipping has constant value "0.0"Constant
amount_due has constant value "0.0"Constant
__table_name__ has constant value "invoices"Constant
due_date has 35 (100.0%) missing valuesMissing
id has unique valuesUnique
order_id has unique valuesUnique
invoice_date has unique valuesUnique
due_date is an unsupported type, check if it needs cleaning or further analysisUnsupported
tax has 35 (100.0%) zerosZeros
shipping has 35 (100.0%) zerosZeros
amount_due has 35 (100.0%) zerosZeros

Reproduction
Analysis started2025-10-30 16:47:16.159970
Analysis finished2025-10-30 16:47:16.214187
Duration0.05 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22
Minimum5
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size443.0 B
2025-10-30T18:47:16.284234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum5
5-th percentile6.7
Q113.5
median22
Q330.5
95-th percentile37.3
Maximum39
Range34
Interquartile range (IQR)17

Descriptive statistics
Standard deviation10.24695077
Coefficient of variation (CV)0.4657704894
Kurtosis-1.2
Mean22
Median Absolute Deviation (MAD)9
Skewness0
Sum770
Variance105
MonotonicityNot monotonic

2025-10-30T18:47:16.391239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
71
 
2.9%
61
 
2.9%
81
 
2.9%
51
 
2.9%
91
 
2.9%
171
 
2.9%
101
 
2.9%
181
 
2.9%
111
 
2.9%
191
 
2.9%
Other values (25)25
71.4%
ValueCountFrequency (%)
51
2.9%
61
2.9%
71
2.9%
81
2.9%
91
2.9%
ValueCountFrequency (%)
391
2.9%
381
2.9%
371
2.9%
361
2.9%
351
2.9%

order_id
Real number (ℝ)

Unique 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.91428571
Minimum30
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size443.0 B
2025-10-30T18:47:16.488348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum30
5-th percentile31.7
Q138.5
median51
Q370.5
95-th percentile77.3
Maximum79
Range49
Interquartile range (IQR)32

Descriptive statistics
Standard deviation16.53582818
Coefficient of variation (CV)0.306705875
Kurtosis-1.511474153
Mean53.91428571
Median Absolute Deviation (MAD)16
Skewness0.1025743054
Sum1887
Variance273.4336134
MonotonicityNot monotonic

2025-10-30T18:47:16.575413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
401
 
2.9%
321
 
2.9%
391
 
2.9%
311
 
2.9%
381
 
2.9%
551
 
2.9%
371
 
2.9%
511
 
2.9%
361
 
2.9%
501
 
2.9%
Other values (25)25
71.4%
ValueCountFrequency (%)
301
2.9%
311
2.9%
321
2.9%
331
2.9%
341
2.9%
ValueCountFrequency (%)
791
2.9%
781
2.9%
771
2.9%
761
2.9%
751
2.9%

invoice_date
Date

Unique 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size408.0 B
Minimum2006-03-22 16:08:59
Maximum2006-04-04 11:43:08
Invalid dates0
Invalid dates (%)0.0%
2025-10-30T18:47:16.656646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:47:16.749014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)

due_date
Unsupported

Missing  Rejected  Unsupported 

Missing35
Missing (%)100.0%
Memory size408.0 B

tax
Real number (ℝ)

Constant  Zeros 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros35
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size408.0 B
2025-10-30T18:47:16.816651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics
Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing

2025-10-30T18:47:16.870687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
035
100.0%
ValueCountFrequency (%)
035
100.0%
ValueCountFrequency (%)
035
100.0%

shipping
Real number (ℝ)

Constant  Zeros 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros35
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size408.0 B
2025-10-30T18:47:16.929230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics
Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing

2025-10-30T18:47:16.981817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
035
100.0%
ValueCountFrequency (%)
035
100.0%
ValueCountFrequency (%)
035
100.0%

amount_due
Real number (ℝ)

Constant  Zeros 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros35
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size408.0 B
2025-10-30T18:47:17.029370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics
Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing

2025-10-30T18:47:17.261246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
035
100.0%
ValueCountFrequency (%)
035
100.0%
ValueCountFrequency (%)
035
100.0%

__table_name__
Text

Constant 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2025-10-30T18:47:17.309581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length8
Median length8
Mean length8
Min length8

Characters and Unicode
Total characters280
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowinvoices
2nd rowinvoices
3rd rowinvoices
4th rowinvoices
5th rowinvoices

ValueCountFrequency (%)
invoices35
100.0%
2025-10-30T18:47:17.407952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i70
25.0%
n35
12.5%
v35
12.5%
o35
12.5%
c35
12.5%
e35
12.5%
s35
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)280
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i70
25.0%
n35
12.5%
v35
12.5%
o35
12.5%
c35
12.5%
e35
12.5%
s35
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)280
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i70
25.0%
n35
12.5%
v35
12.5%
o35
12.5%
c35
12.5%
e35
12.5%
s35
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)280
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i70
25.0%
n35
12.5%
v35
12.5%
o35
12.5%
c35
12.5%
e35
12.5%
s35
12.5%

Report generated by YData.


Table: order_details

Profile: order_details

Overview

Brought to you by YData

Dataset statistics
Number of variables11
Number of observations58
Missing cells103
Missing cells (%)16.1%
Total size in memory9.0 KiB
Average record size in memory158.2 B

Variable types
Numeric9
Unsupported1
Text1

Alerts

discount has constant value "0.0"Constant
__table_name__ has constant value "order_details"Constant
date_allocated has 58 (100.0%) missing valuesMissing
purchase_order_id has 43 (74.1%) missing valuesMissing
inventory_id has 2 (3.4%) missing valuesMissing
id has unique valuesUnique
date_allocated is an unsupported type, check if it needs cleaning or further analysisUnsupported
quantity has 2 (3.4%) zerosZeros
discount has 58 (100.0%) zerosZeros
status_id has 1 (1.7%) zerosZeros

Reproduction
Analysis started2025-10-30 16:47:18.326757
Analysis finished2025-10-30 16:47:18.394582
Duration0.07 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct58
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.25862069
Minimum27
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size650.0 B
2025-10-30T18:47:18.469656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum27
5-th percentile29.85
Q141.25
median55.5
Q373.75
95-th percentile85.15
Maximum91
Range64
Interquartile range (IQR)32.5

Descriptive statistics
Standard deviation18.77901063
Coefficient of variation (CV)0.3279682675
Kurtosis-1.250614934
Mean57.25862069
Median Absolute Deviation (MAD)16.5
Skewness0.08662698586
Sum3321
Variance352.6512402
MonotonicityNot monotonic

2025-10-30T18:47:18.561017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
411
 
1.7%
471
 
1.7%
501
 
1.7%
901
 
1.7%
281
 
1.7%
311
 
1.7%
461
 
1.7%
791
 
1.7%
851
 
1.7%
341
 
1.7%
Other values (48)48
82.8%
ValueCountFrequency (%)
271
1.7%
281
1.7%
291
1.7%
301
1.7%
311
1.7%
ValueCountFrequency (%)
911
1.7%
901
1.7%
861
1.7%
851
1.7%
841
1.7%

order_id
Real number (ℝ)

Distinct40
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.36206897
Minimum30
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size650.0 B
2025-10-30T18:47:18.649080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum30
5-th percentile31
Q140.25
median47.5
Q368.5
95-th percentile79.15
Maximum81
Range51
Interquartile range (IQR)28.25

Descriptive statistics
Standard deviation16.39073152
Coefficient of variation (CV)0.3130268121
Kurtosis-1.176261734
Mean52.36206897
Median Absolute Deviation (MAD)12
Skewness0.4114286915
Sum3037
Variance268.6560799
MonotonicityNot monotonic

2025-10-30T18:47:18.737417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
443
 
5.2%
313
 
5.2%
513
 
5.2%
423
 
5.2%
582
 
3.4%
302
 
3.4%
812
 
3.4%
432
 
3.4%
632
 
3.4%
792
 
3.4%
Other values (30)34
58.6%
ValueCountFrequency (%)
302
3.4%
313
5.2%
322
3.4%
331
 
1.7%
341
 
1.7%
ValueCountFrequency (%)
812
3.4%
801
1.7%
792
3.4%
781
1.7%
771
1.7%

product_id
Real number (ℝ)

Distinct24
Distinct (%)41.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.55172414
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size650.0 B
2025-10-30T18:47:18.804812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum1
5-th percentile3.85
Q117.5
median41
Q351.75
95-th percentile81
Maximum81
Range80
Interquartile range (IQR)34.25

Descriptive statistics
Standard deviation25.7308419
Coefficient of variation (CV)0.6674368651
Kurtosis-1.042721847
Mean38.55172414
Median Absolute Deviation (MAD)22
Skewness0.1803742112
Sum2236
Variance662.076225
MonotonicityNot monotonic

2025-10-30T18:47:18.878731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
435
 
8.6%
485
 
8.6%
814
 
6.9%
804
 
6.9%
194
 
6.9%
84
 
6.9%
414
 
6.9%
343
 
5.2%
403
 
5.2%
562
 
3.4%
Other values (14)20
34.5%
ValueCountFrequency (%)
12
3.4%
31
1.7%
42
3.4%
51
1.7%
62
3.4%
ValueCountFrequency (%)
814
6.9%
804
6.9%
741
 
1.7%
722
3.4%
571
 
1.7%

quantity
Real number (ℝ)

Zeros 

Distinct16
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.72413793
Minimum0
Maximum300
Zeros2
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size592.0 B
2025-10-30T18:47:18.938032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile4.7
Q115
median27.5
Q347.5
95-th percentile215
Maximum300
Range300
Interquartile range (IQR)32.5

Descriptive statistics
Standard deviation70.63143605
Coefficient of variation (CV)1.392462029
Kurtosis6.752549665
Mean50.72413793
Median Absolute Deviation (MAD)15
Skewness2.680008748
Sum2942
Variance4988.799758
MonotonicityNot monotonic

2025-10-30T18:47:19.007948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1010
17.2%
307
12.1%
407
12.1%
256
10.3%
206
10.3%
505
8.6%
1003
 
5.2%
3003
 
5.2%
152
 
3.4%
02
 
3.4%
Other values (6)7
12.1%
ValueCountFrequency (%)
02
 
3.4%
31
 
1.7%
51
 
1.7%
1010
17.2%
152
 
3.4%
ValueCountFrequency (%)
3003
5.2%
2002
3.4%
1003
5.2%
901
 
1.7%
871
 
1.7%

unit_price
Real number (ℝ)

Distinct22
Distinct (%)37.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.27172414
Minimum2.99
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size592.0 B
2025-10-30T18:47:19.077431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum2.99
5-th percentile2.99
Q19.65
median18
Q337.2
95-th percentile47.05
Maximum81
Range78.01
Interquartile range (IQR)27.55

Descriptive statistics
Standard deviation16.90205688
Coefficient of variation (CV)0.7589020399
Kurtosis0.9822667559
Mean22.27172414
Median Absolute Deviation (MAD)8.8
Skewness1.051584481
Sum1291.76
Variance285.6795268
MonotonicityIncreasing

2025-10-30T18:47:19.149238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
465
 
8.6%
12.755
 
8.6%
9.24
 
6.9%
9.654
 
6.9%
2.994
 
6.9%
3.54
 
6.9%
404
 
6.9%
103
 
5.2%
18.43
 
5.2%
143
 
5.2%
Other values (12)19
32.8%
ValueCountFrequency (%)
2.994
6.9%
3.54
6.9%
71
 
1.7%
9.24
6.9%
9.654
6.9%
ValueCountFrequency (%)
811
 
1.7%
532
 
3.4%
465
8.6%
404
6.9%
391
 
1.7%

discount
Real number (ℝ)

Constant  Zeros 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros58
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size592.0 B
2025-10-30T18:47:19.209569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics
Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing

2025-10-30T18:47:19.272933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
058
100.0%
ValueCountFrequency (%)
058
100.0%
ValueCountFrequency (%)
058
100.0%

status_id
Real number (ℝ)

Zeros 

Distinct4
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.896551724
Minimum0
Maximum5
Zeros1
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size650.0 B
2025-10-30T18:47:19.326441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics
Standard deviation0.5830848144
Coefficient of variation (CV)0.3074447203
Kurtosis15.42312292
Mean1.896551724
Median Absolute Deviation (MAD)0
Skewness1.657035387
Sum110
Variance0.3399879008
MonotonicityNot monotonic

2025-10-30T18:47:19.384637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
249
84.5%
17
 
12.1%
51
 
1.7%
01
 
1.7%
ValueCountFrequency (%)
01
 
1.7%
17
 
12.1%
249
84.5%
51
 
1.7%
ValueCountFrequency (%)
51
 
1.7%
249
84.5%
17
 
12.1%
01
 
1.7%

date_allocated
Unsupported

Missing  Rejected  Unsupported 

Missing58
Missing (%)100.0%
Memory size592.0 B

purchase_order_id
Real number (ℝ)

Missing 

Distinct15
Distinct (%)100.0%
Missing43
Missing (%)74.1%
Infinite0
Infinite (%)0.0%
Mean103.0666667
Minimum96
Maximum111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size650.0 B
2025-10-30T18:47:19.438457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum96
5-th percentile96.7
Q199.5
median103
Q3106.5
95-th percentile109.6
Maximum111
Range15
Interquartile range (IQR)7

Descriptive statistics
Standard deviation4.589843861
Coefficient of variation (CV)0.04453276708
Kurtosis-1.043102067
Mean103.0666667
Median Absolute Deviation (MAD)4
Skewness0.09376784045
Sum1546
Variance21.06666667
MonotonicityNot monotonic

2025-10-30T18:47:19.508334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1011
 
1.7%
1031
 
1.7%
971
 
1.7%
981
 
1.7%
1041
 
1.7%
1001
 
1.7%
1091
 
1.7%
1111
 
1.7%
961
 
1.7%
1051
 
1.7%
Other values (5)5
 
8.6%
(Missing)43
74.1%
ValueCountFrequency (%)
961
1.7%
971
1.7%
981
1.7%
991
1.7%
1001
1.7%
ValueCountFrequency (%)
1111
1.7%
1091
1.7%
1081
1.7%
1071
1.7%
1061
1.7%

inventory_id
Real number (ℝ)

Missing 

Distinct56
Distinct (%)100.0%
Missing2
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean100.8035714
Minimum63
Maximum136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size650.0 B
2025-10-30T18:47:19.585304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum63
5-th percentile65.75
Q182.5
median100
Q3122.25
95-th percentile133.25
Maximum136
Range73
Interquartile range (IQR)39.75

Descriptive statistics
Standard deviation23.27262898
Coefficient of variation (CV)0.2308710758
Kurtosis-1.386079178
Mean100.8035714
Median Absolute Deviation (MAD)21
Skewness-0.0788302645
Sum5645
Variance541.6152597
MonotonicityNot monotonic

2025-10-30T18:47:19.678385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
871
 
1.7%
901
 
1.7%
631
 
1.7%
661
 
1.7%
861
 
1.7%
1291
 
1.7%
1351
 
1.7%
731
 
1.7%
691
 
1.7%
1101
 
1.7%
Other values (46)46
79.3%
(Missing)2
 
3.4%
ValueCountFrequency (%)
631
1.7%
641
1.7%
651
1.7%
661
1.7%
671
1.7%
ValueCountFrequency (%)
1361
1.7%
1351
1.7%
1341
1.7%
1331
1.7%
1321
1.7%

__table_name__
Text

Constant 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
2025-10-30T18:47:19.754352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length13
Median length13
Mean length13
Min length13

Characters and Unicode
Total characters754
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st roworder_details
2nd roworder_details
3rd roworder_details
4th roworder_details
5th roworder_details

ValueCountFrequency (%)
order_details58
100.0%
2025-10-30T18:47:19.874602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r116
15.4%
d116
15.4%
e116
15.4%
o58
7.7%
_58
7.7%
t58
7.7%
a58
7.7%
i58
7.7%
l58
7.7%
s58
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r116
15.4%
d116
15.4%
e116
15.4%
o58
7.7%
_58
7.7%
t58
7.7%
a58
7.7%
i58
7.7%
l58
7.7%
s58
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r116
15.4%
d116
15.4%
e116
15.4%
o58
7.7%
_58
7.7%
t58
7.7%
a58
7.7%
i58
7.7%
l58
7.7%
s58
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r116
15.4%
d116
15.4%
e116
15.4%
o58
7.7%
_58
7.7%
t58
7.7%
a58
7.7%
i58
7.7%
l58
7.7%
s58
7.7%

Report generated by YData.


Table: order_details_status

Profile: order_details_status

Overview

Brought to you by YData

Dataset statistics
Number of variables3
Number of observations6
Missing cells0
Missing cells (%)0.0%
Total size in memory1.0 KiB
Average record size in memory171.7 B

Variable types
Numeric1
Text2

Alerts

__table_name__ has constant value "order_details_status"Constant
id has unique valuesUnique
status has unique valuesUnique
id has 1 (16.7%) zerosZeros

Reproduction
Analysis started2025-10-30 16:47:20.929487
Analysis finished2025-10-30 16:47:20.968764
Duration0.04 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique  Zeros 

Distinct6
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5
Minimum0
Maximum5
Zeros1
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size182.0 B
2025-10-30T18:47:21.001941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile0.25
Q11.25
median2.5
Q33.75
95-th percentile4.75
Maximum5
Range5
Interquartile range (IQR)2.5

Descriptive statistics
Standard deviation1.870828693
Coefficient of variation (CV)0.7483314774
Kurtosis-1.2
Mean2.5
Median Absolute Deviation (MAD)1.5
Skewness0
Sum15
Variance3.5
MonotonicityStrictly increasing

2025-10-30T18:47:21.062008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
01
16.7%
11
16.7%
21
16.7%
31
16.7%
41
16.7%
51
16.7%
ValueCountFrequency (%)
01
16.7%
11
16.7%
21
16.7%
31
16.7%
41
16.7%
ValueCountFrequency (%)
51
16.7%
41
16.7%
31
16.7%
21
16.7%
11
16.7%

status
Text

Unique 

Distinct6
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size514.0 B
2025-10-30T18:47:21.122808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length9
Median length8.5
Mean length7.333333333
Min length4

Characters and Unicode
Total characters44
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique6 ?
Unique (%)100.0%

Sample
1st rowNone
2nd rowAllocated
3rd rowInvoiced
4th rowShipped
5th rowOn Order

ValueCountFrequency (%)
none1
12.5%
allocated1
12.5%
invoiced1
12.5%
shipped1
12.5%
on1
12.5%
order1
12.5%
no1
12.5%
stock1
12.5%
2025-10-30T18:47:21.283836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o5
 
11.4%
e5
 
11.4%
d4
 
9.1%
n3
 
6.8%
c3
 
6.8%
N2
 
4.5%
t2
 
4.5%
l2
 
4.5%
O2
 
4.5%
p2
 
4.5%
Other values (10)14
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)44
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o5
 
11.4%
e5
 
11.4%
d4
 
9.1%
n3
 
6.8%
c3
 
6.8%
N2
 
4.5%
t2
 
4.5%
l2
 
4.5%
O2
 
4.5%
p2
 
4.5%
Other values (10)14
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o5
 
11.4%
e5
 
11.4%
d4
 
9.1%
n3
 
6.8%
c3
 
6.8%
N2
 
4.5%
t2
 
4.5%
l2
 
4.5%
O2
 
4.5%
p2
 
4.5%
Other values (10)14
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o5
 
11.4%
e5
 
11.4%
d4
 
9.1%
n3
 
6.8%
c3
 
6.8%
N2
 
4.5%
t2
 
4.5%
l2
 
4.5%
O2
 
4.5%
p2
 
4.5%
Other values (10)14
31.8%

__table_name__
Text

Constant 

Distinct1
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size590.0 B
2025-10-30T18:47:21.361649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length20
Median length20
Mean length20
Min length20

Characters and Unicode
Total characters120
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st roworder_details_status
2nd roworder_details_status
3rd roworder_details_status
4th roworder_details_status
5th roworder_details_status

ValueCountFrequency (%)
order_details_status6
100.0%
2025-10-30T18:47:21.497670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t18
15.0%
s18
15.0%
r12
10.0%
a12
10.0%
d12
10.0%
_12
10.0%
e12
10.0%
o6
 
5.0%
i6
 
5.0%
l6
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)120
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t18
15.0%
s18
15.0%
r12
10.0%
a12
10.0%
d12
10.0%
_12
10.0%
e12
10.0%
o6
 
5.0%
i6
 
5.0%
l6
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)120
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t18
15.0%
s18
15.0%
r12
10.0%
a12
10.0%
d12
10.0%
_12
10.0%
e12
10.0%
o6
 
5.0%
i6
 
5.0%
l6
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)120
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t18
15.0%
s18
15.0%
r12
10.0%
a12
10.0%
d12
10.0%
_12
10.0%
e12
10.0%
o6
 
5.0%
i6
 
5.0%
l6
 
5.0%

Report generated by YData.


Table: orders

Profile: orders

Overview

Brought to you by YData

Dataset statistics
Number of variables21
Number of observations48
Missing cells130
Missing cells (%)12.9%
Total size in memory30.1 KiB
Average record size in memory642.2 B

Variable types
Numeric8
DateTime3
Text8
Unsupported2

Alerts

ship_zip_postal_code has constant value "99999"Constant
ship_country_region has constant value "USA"Constant
taxes has constant value "0.0"Constant
tax_rate has constant value "0.0"Constant
__table_name__ has constant value "orders"Constant
shipped_date has 9 (18.8%) missing valuesMissing
shipper_id has 5 (10.4%) missing valuesMissing
payment_type has 10 (20.8%) missing valuesMissing
paid_date has 10 (20.8%) missing valuesMissing
notes has 48 (100.0%) missing valuesMissing
tax_status_id has 48 (100.0%) missing valuesMissing
id has unique valuesUnique
notes is an unsupported type, check if it needs cleaning or further analysisUnsupported
tax_status_id is an unsupported type, check if it needs cleaning or further analysisUnsupported
shipping_fee has 12 (25.0%) zerosZeros
taxes has 48 (100.0%) zerosZeros
tax_rate has 48 (100.0%) zerosZeros
status_id has 16 (33.3%) zerosZeros

Reproduction
Analysis started2025-10-30 16:47:24.733826
Analysis finished2025-10-30 16:47:24.923709
Duration0.19 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.79166667
Minimum30
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2025-10-30T18:47:25.024644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum30
5-th percentile32.35
Q141.75
median57.5
Q369.25
95-th percentile78.65
Maximum81
Range51
Interquartile range (IQR)27.5

Descriptive statistics
Standard deviation15.74120265
Coefficient of variation (CV)0.282142542
Kurtosis-1.331045073
Mean55.79166667
Median Absolute Deviation (MAD)14
Skewness-0.05609480355
Sum2678
Variance247.785461
MonotonicityNot monotonic

2025-10-30T18:47:25.213467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
321
 
2.1%
301
 
2.1%
311
 
2.1%
341
 
2.1%
331
 
2.1%
721
 
2.1%
651
 
2.1%
381
 
2.1%
451
 
2.1%
711
 
2.1%
Other values (38)38
79.2%
ValueCountFrequency (%)
301
2.1%
311
2.1%
321
2.1%
331
2.1%
341
2.1%
ValueCountFrequency (%)
811
2.1%
801
2.1%
791
2.1%
781
2.1%
771
2.1%

employee_id
Real number (ℝ)

Distinct8
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.458333333
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2025-10-30T18:47:25.339831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum1
5-th percentile1
Q11.75
median4
Q37.25
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)5.5

Descriptive statistics
Standard deviation3.045459357
Coefficient of variation (CV)0.6830936875
Kurtosis-1.356336556
Mean4.458333333
Median Absolute Deviation (MAD)3
Skewness0.3914266675
Sum214
Variance9.274822695
MonotonicityNot monotonic

2025-10-30T18:47:25.445845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
112
25.0%
910
20.8%
48
16.7%
36
12.5%
64
 
8.3%
24
 
8.3%
82
 
4.2%
72
 
4.2%
ValueCountFrequency (%)
112
25.0%
24
 
8.3%
36
12.5%
48
16.7%
64
 
8.3%
ValueCountFrequency (%)
910
20.8%
82
 
4.2%
72
 
4.2%
64
 
8.3%
48
16.7%

customer_id
Real number (ℝ)

Distinct15
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.85416667
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2025-10-30T18:47:25.538575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum1
5-th percentile3
Q16
median8.5
Q325.25
95-th percentile29
Maximum29
Range28
Interquartile range (IQR)19.25

Descriptive statistics
Standard deviation9.815293285
Coefficient of variation (CV)0.7635884565
Kurtosis-1.159827679
Mean12.85416667
Median Absolute Deviation (MAD)4
Skewness0.7421208642
Sum617
Variance96.33998227
MonotonicityNot monotonic

2025-10-30T18:47:25.637882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
86
12.5%
66
12.5%
45
10.4%
284
 
8.3%
104
 
8.3%
294
 
8.3%
33
 
6.2%
12
 
4.2%
272
 
4.2%
122
 
4.2%
Other values (5)10
20.8%
ValueCountFrequency (%)
12
 
4.2%
33
6.2%
45
10.4%
66
12.5%
72
 
4.2%
ValueCountFrequency (%)
294
8.3%
284
8.3%
272
4.2%
262
4.2%
252
4.2%
Distinct28
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Memory size512.0 B
Minimum2006-01-15 00:00:00
Maximum2006-06-23 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-30T18:47:25.722027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:47:25.824322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)

shipped_date
Date

Missing 

Distinct23
Distinct (%)59.0%
Missing9
Missing (%)18.8%
Memory size512.0 B
Minimum2006-01-22 00:00:00
Maximum2006-06-23 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-30T18:47:26.083808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:47:26.230418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)

shipper_id
Real number (ℝ)

Missing 

Distinct3
Distinct (%)7.0%
Missing5
Missing (%)10.4%
Infinite0
Infinite (%)0.0%
Mean2.209302326
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2025-10-30T18:47:26.418659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)1

Descriptive statistics
Standard deviation0.7418811205
Coefficient of variation (CV)0.3357988229
Kurtosis-1.069775714
Mean2.209302326
Median Absolute Deviation (MAD)1
Skewness-0.3613737778
Sum95
Variance0.5503875969
MonotonicityNot monotonic

2025-10-30T18:47:26.527972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
218
37.5%
317
35.4%
18
16.7%
(Missing)5
 
10.4%
ValueCountFrequency (%)
18
16.7%
218
37.5%
317
35.4%
ValueCountFrequency (%)
317
35.4%
218
37.5%
18
16.7%
Distinct15
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2025-10-30T18:47:26.653534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length22
Median length16
Mean length14.20833333
Min length7

Characters and Unicode
Total characters682
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowJohn Edwards
2nd rowKaren Toh
3rd rowChristina Lee
4th rowChristina Lee
5th rowElizabeth Andersen

ValueCountFrequency (%)
lee9
 
9.0%
andersen6
 
6.0%
francisco6
 
6.0%
pérez-olaeta6
 
6.0%
elizabeth6
 
6.0%
christina5
 
5.0%
amritansh4
 
4.0%
raghav4
 
4.0%
roland4
 
4.0%
wacker4
 
4.0%
Other values (19)46
46.0%
2025-10-30T18:47:26.862982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e75
 
11.0%
n64
 
9.4%
a62
 
9.1%
52
 
7.6%
r41
 
6.0%
s32
 
4.7%
i32
 
4.7%
o31
 
4.5%
h30
 
4.4%
t25
 
3.7%
Other values (31)238
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)682
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e75
 
11.0%
n64
 
9.4%
a62
 
9.1%
52
 
7.6%
r41
 
6.0%
s32
 
4.7%
i32
 
4.7%
o31
 
4.5%
h30
 
4.4%
t25
 
3.7%
Other values (31)238
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)682
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e75
 
11.0%
n64
 
9.4%
a62
 
9.1%
52
 
7.6%
r41
 
6.0%
s32
 
4.7%
i32
 
4.7%
o31
 
4.5%
h30
 
4.4%
t25
 
3.7%
Other values (31)238
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)682
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e75
 
11.0%
n64
 
9.4%
a62
 
9.1%
52
 
7.6%
r41
 
6.0%
s32
 
4.7%
i32
 
4.7%
o31
 
4.5%
h30
 
4.4%
t25
 
3.7%
Other values (31)238
34.9%
Distinct15
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2025-10-30T18:47:26.938205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length15
Median length14
Mean length14.45833333
Min length14

Characters and Unicode
Total characters694
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st row123 12th Street
2nd row789 27th Street
3rd row123 4th Street
4th row123 4th Street
5th row123 8th Street

ValueCountFrequency (%)
street48
33.3%
12334
23.6%
78914
 
9.7%
8th6
 
4.2%
6th6
 
4.2%
4th5
 
3.5%
28th4
 
2.8%
10th4
 
2.8%
29th4
 
2.8%
3rd3
 
2.1%
Other values (8)16
 
11.1%
2025-10-30T18:47:27.115586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t141
20.3%
96
13.8%
e96
13.8%
r51
 
7.3%
250
 
7.2%
S48
 
6.9%
146
 
6.6%
h43
 
6.2%
337
 
5.3%
824
 
3.5%
Other values (8)62
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t141
20.3%
96
13.8%
e96
13.8%
r51
 
7.3%
250
 
7.2%
S48
 
6.9%
146
 
6.6%
h43
 
6.2%
337
 
5.3%
824
 
3.5%
Other values (8)62
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t141
20.3%
96
13.8%
e96
13.8%
r51
 
7.3%
250
 
7.2%
S48
 
6.9%
146
 
6.6%
h43
 
6.2%
337
 
5.3%
824
 
3.5%
Other values (8)62
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t141
20.3%
96
13.8%
e96
13.8%
r51
 
7.3%
250
 
7.2%
S48
 
6.9%
146
 
6.6%
h43
 
6.2%
337
 
5.3%
824
 
3.5%
Other values (8)62
8.9%
Distinct12
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2025-10-30T18:47:27.211016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length14
Median length9
Mean length7.854166667
Min length5

Characters and Unicode
Total characters377
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowLas Vegas
2nd rowLas Vegas
3rd rowNew York
4th rowNew York
5th rowPortland

ValueCountFrequency (%)
portland6
 
9.4%
milwaukee6
 
9.4%
chicago6
 
9.4%
new5
 
7.8%
york5
 
7.8%
memphis4
 
6.2%
las4
 
6.2%
vegas4
 
6.2%
denver4
 
6.2%
miami4
 
6.2%
Other values (7)16
25.0%
2025-10-30T18:47:27.381220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e44
 
11.7%
a39
 
10.3%
i28
 
7.4%
o22
 
5.8%
s20
 
5.3%
l19
 
5.0%
16
 
4.2%
r15
 
4.0%
t14
 
3.7%
M14
 
3.7%
Other values (22)146
38.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)377
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e44
 
11.7%
a39
 
10.3%
i28
 
7.4%
o22
 
5.8%
s20
 
5.3%
l19
 
5.0%
16
 
4.2%
r15
 
4.0%
t14
 
3.7%
M14
 
3.7%
Other values (22)146
38.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)377
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e44
 
11.7%
a39
 
10.3%
i28
 
7.4%
o22
 
5.8%
s20
 
5.3%
l19
 
5.0%
16
 
4.2%
r15
 
4.0%
t14
 
3.7%
M14
 
3.7%
Other values (22)146
38.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)377
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e44
 
11.7%
a39
 
10.3%
i28
 
7.4%
o22
 
5.8%
s20
 
5.3%
l19
 
5.0%
16
 
4.2%
r15
 
4.0%
t14
 
3.7%
M14
 
3.7%
Other values (22)146
38.7%
Distinct12
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2025-10-30T18:47:27.472032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length2
Median length2
Mean length2
Min length2

Characters and Unicode
Total characters96
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowNV
2nd rowNV
3rd rowNY
4th rowNY
5th rowOR

ValueCountFrequency (%)
or6
12.5%
wi6
12.5%
il6
12.5%
ny5
10.4%
tn4
8.3%
nv4
8.3%
co4
8.3%
fl4
8.3%
ca3
6.2%
wa2
 
4.2%
Other values (2)4
8.3%
2025-10-30T18:47:27.635540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I14
14.6%
N13
13.5%
L10
10.4%
O10
10.4%
W8
8.3%
C7
7.3%
R6
6.2%
T6
6.2%
Y5
 
5.2%
A5
 
5.2%
Other values (4)12
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)96
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I14
14.6%
N13
13.5%
L10
10.4%
O10
10.4%
W8
8.3%
C7
7.3%
R6
6.2%
T6
6.2%
Y5
 
5.2%
A5
 
5.2%
Other values (4)12
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)96
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I14
14.6%
N13
13.5%
L10
10.4%
O10
10.4%
W8
8.3%
C7
7.3%
R6
6.2%
T6
6.2%
Y5
 
5.2%
A5
 
5.2%
Other values (4)12
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)96
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I14
14.6%
N13
13.5%
L10
10.4%
O10
10.4%
W8
8.3%
C7
7.3%
R6
6.2%
T6
6.2%
Y5
 
5.2%
A5
 
5.2%
Other values (4)12
12.5%

ship_zip_postal_code
Text

Constant 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2025-10-30T18:47:27.704549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length5
Median length5
Mean length5
Min length5

Characters and Unicode
Total characters240
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st row99999
2nd row99999
3rd row99999
4th row99999
5th row99999

ValueCountFrequency (%)
9999948
100.0%
2025-10-30T18:47:27.852840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9240
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9240
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9240
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9240
100.0%

ship_country_region
Text

Constant 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2025-10-30T18:47:27.907409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length3
Median length3
Mean length3
Min length3

Characters and Unicode
Total characters144
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowUSA
2nd rowUSA
3rd rowUSA
4th rowUSA
5th rowUSA

ValueCountFrequency (%)
usa48
100.0%
2025-10-30T18:47:28.023037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U48
33.3%
S48
33.3%
A48
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)144
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U48
33.3%
S48
33.3%
A48
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)144
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U48
33.3%
S48
33.3%
A48
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)144
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U48
33.3%
S48
33.3%
A48
33.3%

shipping_fee
Real number (ℝ)

Zeros 

Distinct13
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.54166667
Minimum0
Maximum300
Zeros12
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size512.0 B
2025-10-30T18:47:28.184291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile0
Q13
median7
Q350
95-th percentile200
Maximum300
Range300
Interquartile range (IQR)47

Descriptive statistics
Standard deviation78.04089444
Coefficient of variation (CV)1.752087434
Kurtosis3.942468244
Mean44.54166667
Median Absolute Deviation (MAD)7
Skewness2.175695331
Sum2138
Variance6090.381206
MonotonicityNot monotonic

2025-10-30T18:47:28.298513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
012
25.0%
58
16.7%
2004
 
8.3%
74
 
8.3%
504
 
8.3%
402
 
4.2%
42
 
4.2%
122
 
4.2%
102
 
4.2%
3002
 
4.2%
Other values (3)6
12.5%
ValueCountFrequency (%)
012
25.0%
42
 
4.2%
58
16.7%
74
 
8.3%
92
 
4.2%
ValueCountFrequency (%)
3002
4.2%
2004
8.3%
1002
4.2%
602
4.2%
504
8.3%

taxes
Real number (ℝ)

Constant  Zeros 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros48
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size512.0 B
2025-10-30T18:47:28.450808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics
Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing

2025-10-30T18:47:28.549772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
048
100.0%
ValueCountFrequency (%)
048
100.0%
ValueCountFrequency (%)
048
100.0%

payment_type
Text

Missing 

Distinct3
Distinct (%)7.9%
Missing10
Missing (%)20.8%
Memory size2.8 KiB
2025-10-30T18:47:28.658224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length11
Median length5
Mean length7.421052632
Min length4

Characters and Unicode
Total characters282
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowCredit Card
2nd rowCheck
3rd rowCredit Card
4th rowCheck
5th rowCredit Card

ValueCountFrequency (%)
check18
33.3%
credit16
29.6%
card16
29.6%
cash4
 
7.4%
2025-10-30T18:47:28.873009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C54
19.1%
e34
12.1%
d32
11.3%
r32
11.3%
h22
7.8%
a20
 
7.1%
c18
 
6.4%
k18
 
6.4%
i16
 
5.7%
t16
 
5.7%
Other values (2)20
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)282
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C54
19.1%
e34
12.1%
d32
11.3%
r32
11.3%
h22
7.8%
a20
 
7.1%
c18
 
6.4%
k18
 
6.4%
i16
 
5.7%
t16
 
5.7%
Other values (2)20
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)282
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C54
19.1%
e34
12.1%
d32
11.3%
r32
11.3%
h22
7.8%
a20
 
7.1%
c18
 
6.4%
k18
 
6.4%
i16
 
5.7%
t16
 
5.7%
Other values (2)20
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)282
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C54
19.1%
e34
12.1%
d32
11.3%
r32
11.3%
h22
7.8%
a20
 
7.1%
c18
 
6.4%
k18
 
6.4%
i16
 
5.7%
t16
 
5.7%
Other values (2)20
 
7.1%

paid_date
Date

Missing 

Distinct26
Distinct (%)68.4%
Missing10
Missing (%)20.8%
Memory size512.0 B
Minimum2006-01-15 00:00:00
Maximum2006-06-23 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-30T18:47:28.951294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:47:29.095152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)

notes
Unsupported

Missing  Rejected  Unsupported 

Missing48
Missing (%)100.0%
Memory size512.0 B

tax_rate
Real number (ℝ)

Constant  Zeros 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros48
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size512.0 B
2025-10-30T18:47:29.171890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics
Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing

2025-10-30T18:47:29.239948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
048
100.0%
ValueCountFrequency (%)
048
100.0%
ValueCountFrequency (%)
048
100.0%

tax_status_id
Unsupported

Missing  Rejected  Unsupported 

Missing48
Missing (%)100.0%
Memory size560.0 B

status_id
Real number (ℝ)

Zeros 

Distinct3
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.979166667
Minimum0
Maximum3
Zeros16
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size560.0 B
2025-10-30T18:47:29.343255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile0
Q10
median3
Q33
95-th percentile3
Maximum3
Range3
Interquartile range (IQR)3

Descriptive statistics
Standard deviation1.421560176
Coefficient of variation (CV)0.7182619835
Kurtosis-1.548849887
Mean1.979166667
Median Absolute Deviation (MAD)0
Skewness-0.7037744749
Sum95
Variance2.020833333
MonotonicityNot monotonic

2025-10-30T18:47:29.473699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
331
64.6%
016
33.3%
21
 
2.1%
ValueCountFrequency (%)
016
33.3%
21
 
2.1%
331
64.6%
ValueCountFrequency (%)
331
64.6%
21
 
2.1%
016
33.3%

__table_name__
Text

Constant 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2025-10-30T18:47:29.565762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length6
Median length6
Mean length6
Min length6

Characters and Unicode
Total characters288
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st roworders
2nd roworders
3rd roworders
4th roworders
5th roworders

ValueCountFrequency (%)
orders48
100.0%
2025-10-30T18:47:29.711818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r96
33.3%
o48
16.7%
d48
16.7%
e48
16.7%
s48
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)288
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r96
33.3%
o48
16.7%
d48
16.7%
e48
16.7%
s48
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)288
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r96
33.3%
o48
16.7%
d48
16.7%
e48
16.7%
s48
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)288
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r96
33.3%
o48
16.7%
d48
16.7%
e48
16.7%
s48
16.7%

Report generated by YData.


Table: orders_status

Profile: orders_status

Overview

Brought to you by YData

Dataset statistics
Number of variables3
Number of observations4
Missing cells0
Missing cells (%)0.0%
Total size in memory696.0 B
Average record size in memory174.0 B

Variable types
Numeric1
Text2

Alerts

__table_name__ has constant value "orders_status"Constant
id has unique valuesUnique
status_name has unique valuesUnique
id has 1 (25.0%) zerosZeros

Reproduction
Analysis started2025-10-30 16:47:30.663506
Analysis finished2025-10-30 16:47:30.705684
Duration0.04 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique  Zeros 

Distinct4
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5
Minimum0
Maximum3
Zeros1
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size164.0 B
2025-10-30T18:47:30.765149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile0.15
Q10.75
median1.5
Q32.25
95-th percentile2.85
Maximum3
Range3
Interquartile range (IQR)1.5

Descriptive statistics
Standard deviation1.290994449
Coefficient of variation (CV)0.8606629658
Kurtosis-1.2
Mean1.5
Median Absolute Deviation (MAD)1
Skewness0
Sum6
Variance1.666666667
MonotonicityStrictly increasing

2025-10-30T18:47:30.842618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
01
25.0%
11
25.0%
21
25.0%
31
25.0%
ValueCountFrequency (%)
01
25.0%
11
25.0%
21
25.0%
31
25.0%
ValueCountFrequency (%)
31
25.0%
21
25.0%
11
25.0%
01
25.0%

status_name
Text

Unique 

Distinct4
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2025-10-30T18:47:30.937510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length8
Median length6.5
Mean length6
Min length3

Characters and Unicode
Total characters24
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique4 ?
Unique (%)100.0%

Sample
1st rowNew
2nd rowInvoiced
3rd rowShipped
4th rowClosed

ValueCountFrequency (%)
new1
25.0%
invoiced1
25.0%
shipped1
25.0%
closed1
25.0%
2025-10-30T18:47:31.115669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e4
16.7%
d3
12.5%
o2
 
8.3%
i2
 
8.3%
p2
 
8.3%
w1
 
4.2%
I1
 
4.2%
N1
 
4.2%
n1
 
4.2%
v1
 
4.2%
Other values (6)6
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e4
16.7%
d3
12.5%
o2
 
8.3%
i2
 
8.3%
p2
 
8.3%
w1
 
4.2%
I1
 
4.2%
N1
 
4.2%
n1
 
4.2%
v1
 
4.2%
Other values (6)6
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e4
16.7%
d3
12.5%
o2
 
8.3%
i2
 
8.3%
p2
 
8.3%
w1
 
4.2%
I1
 
4.2%
N1
 
4.2%
n1
 
4.2%
v1
 
4.2%
Other values (6)6
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e4
16.7%
d3
12.5%
o2
 
8.3%
i2
 
8.3%
p2
 
8.3%
w1
 
4.2%
I1
 
4.2%
N1
 
4.2%
n1
 
4.2%
v1
 
4.2%
Other values (6)6
25.0%

__table_name__
Text

Constant 

Distinct1
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size408.0 B
2025-10-30T18:47:31.177243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length13
Median length13
Mean length13
Min length13

Characters and Unicode
Total characters52
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st roworders_status
2nd roworders_status
3rd roworders_status
4th roworders_status

ValueCountFrequency (%)
orders_status4
100.0%
2025-10-30T18:47:31.365465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s12
23.1%
t8
15.4%
r8
15.4%
o4
 
7.7%
d4
 
7.7%
e4
 
7.7%
_4
 
7.7%
a4
 
7.7%
u4
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)52
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s12
23.1%
t8
15.4%
r8
15.4%
o4
 
7.7%
d4
 
7.7%
e4
 
7.7%
_4
 
7.7%
a4
 
7.7%
u4
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)52
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s12
23.1%
t8
15.4%
r8
15.4%
o4
 
7.7%
d4
 
7.7%
e4
 
7.7%
_4
 
7.7%
a4
 
7.7%
u4
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)52
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s12
23.1%
t8
15.4%
r8
15.4%
o4
 
7.7%
d4
 
7.7%
e4
 
7.7%
_4
 
7.7%
a4
 
7.7%
u4
 
7.7%

Report generated by YData.


Table: orders_tax_status

Profile: orders_tax_status

Overview

Brought to you by YData

Dataset statistics
Number of variables3
Number of observations2
Missing cells0
Missing cells (%)0.0%
Total size in memory425.0 B
Average record size in memory212.5 B

Variable types
Numeric1
Text2

Alerts

__table_name__ has constant value "orders_tax_status"Constant
id has unique valuesUnique
tax_status_name has unique valuesUnique
id has 1 (50.0%) zerosZeros

Reproduction
Analysis started2025-10-30 16:47:32.212133
Analysis finished2025-10-30 16:47:32.263927
Duration0.05 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique  Zeros 

Distinct2
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5
Minimum0
Maximum1
Zeros1
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size146.0 B
2025-10-30T18:47:32.318923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile0.05
Q10.25
median0.5
Q30.75
95-th percentile0.95
Maximum1
Range1
Interquartile range (IQR)0.5

Descriptive statistics
2025-10-30T18:47:32.431763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
Standard deviation0.7071067812
Coefficient of variation (CV)1.414213562
Kurtosis
Mean0.5
Median Absolute Deviation (MAD)0.5
Skewness
Sum1
Variance0.5
MonotonicityStrictly increasing
ValueCountFrequency (%)
01
50.0%
11
50.0%

ValueCountFrequency (%)
01
50.0%
11
50.0%
ValueCountFrequency (%)
11
50.0%
01
50.0%

tax_status_name
Text

Unique 

Distinct2
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size259.0 B
2025-10-30T18:47:32.561604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length10
Median length8.5
Mean length8.5
Min length7

Characters and Unicode
Total characters17
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique2 ?
Unique (%)100.0%

Sample
1st rowTax Exempt
2nd rowTaxable

ValueCountFrequency (%)
tax1
33.3%
exempt1
33.3%
taxable1
33.3%
2025-10-30T18:47:32.767855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a3
17.6%
x3
17.6%
T2
11.8%
e2
11.8%
1
 
5.9%
E1
 
5.9%
m1
 
5.9%
p1
 
5.9%
t1
 
5.9%
b1
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)17
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a3
17.6%
x3
17.6%
T2
11.8%
e2
11.8%
1
 
5.9%
E1
 
5.9%
m1
 
5.9%
p1
 
5.9%
t1
 
5.9%
b1
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)17
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a3
17.6%
x3
17.6%
T2
11.8%
e2
11.8%
1
 
5.9%
E1
 
5.9%
m1
 
5.9%
p1
 
5.9%
t1
 
5.9%
b1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)17
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a3
17.6%
x3
17.6%
T2
11.8%
e2
11.8%
1
 
5.9%
E1
 
5.9%
m1
 
5.9%
p1
 
5.9%
t1
 
5.9%
b1
 
5.9%

__table_name__
Text

Constant 

Distinct1
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size276.0 B
2025-10-30T18:47:32.841074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length17
Median length17
Mean length17
Min length17

Characters and Unicode
Total characters34
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st roworders_tax_status
2nd roworders_tax_status

ValueCountFrequency (%)
orders_tax_status2
100.0%
2025-10-30T18:47:33.030505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t6
17.6%
s6
17.6%
a4
11.8%
r4
11.8%
_4
11.8%
o2
 
5.9%
d2
 
5.9%
e2
 
5.9%
x2
 
5.9%
u2
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)34
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t6
17.6%
s6
17.6%
a4
11.8%
r4
11.8%
_4
11.8%
o2
 
5.9%
d2
 
5.9%
e2
 
5.9%
x2
 
5.9%
u2
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)34
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t6
17.6%
s6
17.6%
a4
11.8%
r4
11.8%
_4
11.8%
o2
 
5.9%
d2
 
5.9%
e2
 
5.9%
x2
 
5.9%
u2
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)34
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t6
17.6%
s6
17.6%
a4
11.8%
r4
11.8%
_4
11.8%
o2
 
5.9%
d2
 
5.9%
e2
 
5.9%
x2
 
5.9%
u2
 
5.9%

Report generated by YData.


Table: privileges

Profile: privileges

Overview

Brought to you by YData

Dataset statistics
Number of variables3
Number of observations1
Missing cells0
Missing cells (%)0.0%
Total size in memory279.0 B
Average record size in memory279.0 B

Variable types
Numeric1
Text2

Alerts

id has constant value "2"Constant
privilege_name has constant value "Purchase Approvals"Constant
__table_name__ has constant value "privileges"Constant
id has unique valuesUnique
privilege_name has unique valuesUnique
__table_name__ has unique valuesUnique

Reproduction
Analysis started2025-10-30 16:47:34.003156
Analysis finished2025-10-30 16:47:34.052137
Duration0.05 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Constant  Unique 

Distinct1
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2
Minimum2
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size137.0 B
2025-10-30T18:47:34.095194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum2
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum2
Range0
Interquartile range (IQR)0

Descriptive statistics
2025-10-30T18:47:34.197146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
Standard deviation
Coefficient of variation (CV)
Kurtosis
Mean2
Median Absolute Deviation (MAD)0
Skewness
Sum2
Variance
MonotonicityStrictly increasing
ValueCountFrequency (%)
21
100.0%

ValueCountFrequency (%)
21
100.0%
ValueCountFrequency (%)
21
100.0%

privilege_name
Text

Constant  Unique 

Distinct1
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size203.0 B
2025-10-30T18:47:34.293466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length18
Median length18
Mean length18
Min length18

Characters and Unicode
Total characters18
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique1 ?
Unique (%)100.0%

Sample
1st rowPurchase Approvals

ValueCountFrequency (%)
purchase1
50.0%
approvals1
50.0%
2025-10-30T18:47:34.451061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a2
11.1%
r2
11.1%
p2
11.1%
s2
11.1%
u1
 
5.6%
P1
 
5.6%
h1
 
5.6%
c1
 
5.6%
1
 
5.6%
e1
 
5.6%
Other values (4)4
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)18
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a2
11.1%
r2
11.1%
p2
11.1%
s2
11.1%
u1
 
5.6%
P1
 
5.6%
h1
 
5.6%
c1
 
5.6%
1
 
5.6%
e1
 
5.6%
Other values (4)4
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a2
11.1%
r2
11.1%
p2
11.1%
s2
11.1%
u1
 
5.6%
P1
 
5.6%
h1
 
5.6%
c1
 
5.6%
1
 
5.6%
e1
 
5.6%
Other values (4)4
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a2
11.1%
r2
11.1%
p2
11.1%
s2
11.1%
u1
 
5.6%
P1
 
5.6%
h1
 
5.6%
c1
 
5.6%
1
 
5.6%
e1
 
5.6%
Other values (4)4
22.2%

__table_name__
Text

Constant  Unique 

Distinct1
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size195.0 B
2025-10-30T18:47:34.520378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length10
Median length10
Mean length10
Min length10

Characters and Unicode
Total characters10
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique1 ?
Unique (%)100.0%

Sample
1st rowprivileges

ValueCountFrequency (%)
privileges1
100.0%
2025-10-30T18:47:34.720874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i2
20.0%
e2
20.0%
r1
10.0%
p1
10.0%
v1
10.0%
l1
10.0%
g1
10.0%
s1
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)10
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i2
20.0%
e2
20.0%
r1
10.0%
p1
10.0%
v1
10.0%
l1
10.0%
g1
10.0%
s1
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i2
20.0%
e2
20.0%
r1
10.0%
p1
10.0%
v1
10.0%
l1
10.0%
g1
10.0%
s1
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i2
20.0%
e2
20.0%
r1
10.0%
p1
10.0%
v1
10.0%
l1
10.0%
g1
10.0%
s1
10.0%

Report generated by YData.


Table: products

Profile: products

Overview

Brought to you by YData

Dataset statistics
Number of variables15
Number of observations45
Missing cells65
Missing cells (%)9.6%
Total size in memory24.3 KiB
Average record size in memory552.8 B

Variable types
Text7
Numeric7
Unsupported1

Alerts

discontinued has constant value "0"Constant
attachments has constant value ""Constant
__table_name__ has constant value "products"Constant
description has 45 (100.0%) missing valuesMissing
quantity_per_unit has 5 (11.1%) missing valuesMissing
minimum_reorder_quantity has 15 (33.3%) missing valuesMissing
id has unique valuesUnique
product_name has unique valuesUnique
description is an unsupported type, check if it needs cleaning or further analysisUnsupported
discontinued has 45 (100.0%) zerosZeros

Reproduction
Analysis started2025-10-30 16:47:35.634435
Analysis finished2025-10-30 16:47:35.803163
Duration0.17 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct12
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2025-10-30T18:47:35.880455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length3
Median length1
Mean length1.333333333
Min length1

Characters and Unicode
Total characters60
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique4 ?
Unique (%)8.9%

Sample
1st row6
2nd row6
3rd row6
4th row6
5th row6

ValueCountFrequency (%)
611
24.4%
19
20.0%
105
11.1%
74
 
8.9%
2;64
 
8.9%
23
 
6.7%
83
 
6.7%
42
 
4.4%
91
 
2.2%
51
 
2.2%
Other values (2)2
 
4.4%
2025-10-30T18:47:36.104987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
615
25.0%
114
23.3%
27
11.7%
05
 
8.3%
;5
 
8.3%
74
 
6.7%
83
 
5.0%
43
 
5.0%
32
 
3.3%
91
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)60
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
615
25.0%
114
23.3%
27
11.7%
05
 
8.3%
;5
 
8.3%
74
 
6.7%
83
 
5.0%
43
 
5.0%
32
 
3.3%
91
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)60
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
615
25.0%
114
23.3%
27
11.7%
05
 
8.3%
;5
 
8.3%
74
 
6.7%
83
 
5.0%
43
 
5.0%
32
 
3.3%
91
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)60
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
615
25.0%
114
23.3%
27
11.7%
05
 
8.3%
;5
 
8.3%
74
 
6.7%
83
 
5.0%
43
 
5.0%
32
 
3.3%
91
 
1.7%

id
Real number (ℝ)

Unique 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.93333333
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size533.0 B
2025-10-30T18:47:36.195442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum1
5-th percentile4.2
Q121
median66
Q388
95-th percentile96.8
Maximum99
Range98
Interquartile range (IQR)67

Descriptive statistics
Standard deviation33.75001684
Coefficient of variation (CV)0.5825664586
Kurtosis-1.345925958
Mean57.93333333
Median Absolute Deviation (MAD)25
Skewness-0.4390602797
Sum2607
Variance1139.063636
MonotonicityNot monotonic

2025-10-30T18:47:36.348857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
881
 
2.2%
891
 
2.2%
931
 
2.2%
941
 
2.2%
921
 
2.2%
911
 
2.2%
901
 
2.2%
821
 
2.2%
871
 
2.2%
961
 
2.2%
Other values (35)35
77.8%
ValueCountFrequency (%)
11
2.2%
31
2.2%
41
2.2%
51
2.2%
61
2.2%
ValueCountFrequency (%)
991
2.2%
981
2.2%
971
2.2%
961
2.2%
951
2.2%
Distinct43
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2025-10-30T18:47:36.547900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length9
Median length8
Mean length7.866666667
Min length6

Characters and Unicode
Total characters354
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique41 ?
Unique (%)91.1%

Sample
1st rowNWTCFV-88
2nd rowNWTCFV-89
3rd rowNWTCFV-93
4th rowNWTCFV-94
5th rowNWTCFV-92

ValueCountFrequency (%)
nwtc-822
 
4.4%
nwtjp-62
 
4.4%
nwtcfv-891
 
2.2%
nwtcfv-931
 
2.2%
nwtcfv-941
 
2.2%
nwtcfv-911
 
2.2%
nwtcfv-921
 
2.2%
nwtcfv-901
 
2.2%
nwtb-871
 
2.2%
nwtcm-961
 
2.2%
Other values (33)33
73.3%
2025-10-30T18:47:36.826974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N50
14.1%
W45
12.7%
T45
12.7%
-45
12.7%
C18
 
5.1%
814
 
4.0%
F13
 
3.7%
912
 
3.4%
49
 
2.5%
19
 
2.5%
Other values (16)94
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)354
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N50
14.1%
W45
12.7%
T45
12.7%
-45
12.7%
C18
 
5.1%
814
 
4.0%
F13
 
3.7%
912
 
3.4%
49
 
2.5%
19
 
2.5%
Other values (16)94
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)354
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N50
14.1%
W45
12.7%
T45
12.7%
-45
12.7%
C18
 
5.1%
814
 
4.0%
F13
 
3.7%
912
 
3.4%
49
 
2.5%
19
 
2.5%
Other values (16)94
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)354
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N50
14.1%
W45
12.7%
T45
12.7%
-45
12.7%
C18
 
5.1%
814
 
4.0%
F13
 
3.7%
912
 
3.4%
49
 
2.5%
19
 
2.5%
Other values (16)94
26.6%

product_name
Text

Unique 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2025-10-30T18:47:36.966667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length40
Median length33
Mean length27.8
Min length21

Characters and Unicode
Total characters1251
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique45 ?
Unique (%)100.0%

Sample
1st rowNorthwind Traders Pears
2nd rowNorthwind Traders Peaches
3rd rowNorthwind Traders Corn
4th rowNorthwind Traders Peas
5th rowNorthwind Traders Green Beans

ValueCountFrequency (%)
northwind45
27.4%
traders45
27.4%
mix3
 
1.8%
dried3
 
1.8%
sauce3
 
1.8%
chocolate2
 
1.2%
tea2
 
1.2%
pears2
 
1.2%
hot2
 
1.2%
green2
 
1.2%
Other values (54)55
33.5%
2025-10-30T18:47:37.423306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r163
13.0%
119
 
9.5%
d99
 
7.9%
e96
 
7.7%
a84
 
6.7%
o73
 
5.8%
i72
 
5.8%
n66
 
5.3%
s62
 
5.0%
t59
 
4.7%
Other values (33)358
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1251
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r163
13.0%
119
 
9.5%
d99
 
7.9%
e96
 
7.7%
a84
 
6.7%
o73
 
5.8%
i72
 
5.8%
n66
 
5.3%
s62
 
5.0%
t59
 
4.7%
Other values (33)358
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1251
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r163
13.0%
119
 
9.5%
d99
 
7.9%
e96
 
7.7%
a84
 
6.7%
o73
 
5.8%
i72
 
5.8%
n66
 
5.3%
s62
 
5.0%
t59
 
4.7%
Other values (33)358
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1251
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r163
13.0%
119
 
9.5%
d99
 
7.9%
e96
 
7.7%
a84
 
6.7%
o73
 
5.8%
i72
 
5.8%
n66
 
5.3%
s62
 
5.0%
t59
 
4.7%
Other values (33)358
28.6%

description
Unsupported

Missing  Rejected  Unsupported 

Missing45
Missing (%)100.0%
Memory size488.0 B

standard_cost
Real number (ℝ)

Distinct29
Distinct (%)64.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.6825
Minimum0.5
Maximum60.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-10-30T18:47:37.548063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0.5
5-th percentile1
Q12
median7.5
Q316.0125
95-th percentile33.6
Maximum60.75
Range60.25
Interquartile range (IQR)14.0125

Descriptive statistics
Standard deviation12.68946089
Coefficient of variation (CV)1.086193956
Kurtosis4.054435737
Mean11.6825
Median Absolute Deviation (MAD)6.5
Skewness1.794124107
Sum525.7125
Variance161.0224176
MonotonicityNot monotonic

2025-10-30T18:47:37.687099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
19
20.0%
24
 
8.9%
7.53
 
6.7%
32
 
4.4%
0.52
 
4.4%
10.52
 
4.4%
6.91
 
2.2%
91
 
2.2%
16.01251
 
2.2%
29.251
 
2.2%
Other values (19)19
42.2%
ValueCountFrequency (%)
0.52
 
4.4%
19
20.0%
24
8.9%
32
 
4.4%
5.251
 
2.2%
ValueCountFrequency (%)
60.751
2.2%
39.751
2.2%
34.51
2.2%
301
2.2%
29.251
2.2%

list_price
Real number (ℝ)

Distinct37
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.84577778
Minimum1.2
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2025-10-30T18:47:37.844120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum1.2
5-th percentile1.34
Q12.99
median10
Q321.35
95-th percentile44.8
Maximum81
Range79.8
Interquartile range (IQR)18.36

Descriptive statistics
Standard deviation16.74302236
Coefficient of variation (CV)1.056623575
Kurtosis4.20573241
Mean15.84577778
Median Absolute Deviation (MAD)8.11
Skewness1.8263175
Sum713.06
Variance280.3287977
MonotonicityNot monotonic

2025-10-30T18:47:37.999536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
43
 
6.7%
103
 
6.7%
1.22
 
4.4%
22
 
4.4%
1.52
 
4.4%
1.82
 
4.4%
1.31
 
2.2%
1.891
 
2.2%
51
 
2.2%
1.951
 
2.2%
Other values (27)27
60.0%
ValueCountFrequency (%)
1.22
4.4%
1.31
2.2%
1.52
4.4%
1.82
4.4%
1.891
2.2%
ValueCountFrequency (%)
811
2.2%
531
2.2%
461
2.2%
401
2.2%
391
2.2%

reorder_level
Real number (ℝ)

Distinct8
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.44444444
Minimum5
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size533.0 B
2025-10-30T18:47:38.122776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum5
5-th percentile6
Q110
median10
Q325
95-th percentile90
Maximum100
Range95
Interquartile range (IQR)15

Descriptive statistics
Standard deviation23.44292446
Coefficient of variation (CV)1.044486733
Kurtosis6.313945684
Mean22.44444444
Median Absolute Deviation (MAD)5
Skewness2.559187705
Sum1010
Variance549.5707071
MonotonicityNot monotonic

2025-10-30T18:47:38.190491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1021
46.7%
305
 
11.1%
255
 
11.1%
204
 
8.9%
53
 
6.7%
1003
 
6.7%
502
 
4.4%
152
 
4.4%
ValueCountFrequency (%)
53
 
6.7%
1021
46.7%
152
 
4.4%
204
 
8.9%
255
 
11.1%
ValueCountFrequency (%)
1003
6.7%
502
 
4.4%
305
11.1%
255
11.1%
204
8.9%

target_level
Real number (ℝ)

Distinct10
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.55555556
Minimum20
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size533.0 B
2025-10-30T18:47:38.252297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum20
5-th percentile20
Q140
median40
Q3100
95-th percentile200
Maximum200
Range180
Interquartile range (IQR)60

Descriptive statistics
Standard deviation50.50677522
Coefficient of variation (CV)0.726135746
Kurtosis1.711244811
Mean69.55555556
Median Absolute Deviation (MAD)20
Skewness1.542215931
Sum3130
Variance2550.934343
MonotonicityNot monotonic

2025-10-30T18:47:38.344133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4019
42.2%
1006
 
13.3%
205
 
11.1%
2004
 
8.9%
503
 
6.7%
602
 
4.4%
802
 
4.4%
1202
 
4.4%
751
 
2.2%
1251
 
2.2%
ValueCountFrequency (%)
205
 
11.1%
4019
42.2%
503
 
6.7%
602
 
4.4%
751
 
2.2%
ValueCountFrequency (%)
2004
8.9%
1251
 
2.2%
1202
 
4.4%
1006
13.3%
802
 
4.4%

quantity_per_unit
Text

Missing 

Distinct32
Distinct (%)80.0%
Missing5
Missing (%)11.1%
Memory size3.0 KiB
2025-10-30T18:47:38.485048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length20
Median length17.5
Mean length12.25
Min length4

Characters and Unicode
Total characters490
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique28 ?
Unique (%)70.0%

Sample
1st row15.25 OZ
2nd row15.25 OZ
3rd row14.5 OZ
4th row14.5 OZ
5th row14.5 OZ

ValueCountFrequency (%)
oz18
 
12.2%
17
 
11.6%
1210
 
6.8%
pkgs8
 
5.4%
boxes8
 
5.4%
247
 
4.8%
g6
 
4.1%
15.255
 
3.4%
jars4
 
2.7%
14.53
 
2.0%
Other values (37)61
41.5%
2025-10-30T18:47:38.695912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
107
21.8%
s30
 
6.1%
229
 
5.9%
127
 
5.5%
o24
 
4.9%
522
 
4.5%
g21
 
4.3%
021
 
4.3%
b18
 
3.7%
-17
 
3.5%
Other values (23)174
35.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)490
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
107
21.8%
s30
 
6.1%
229
 
5.9%
127
 
5.5%
o24
 
4.9%
522
 
4.5%
g21
 
4.3%
021
 
4.3%
b18
 
3.7%
-17
 
3.5%
Other values (23)174
35.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)490
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
107
21.8%
s30
 
6.1%
229
 
5.9%
127
 
5.5%
o24
 
4.9%
522
 
4.5%
g21
 
4.3%
021
 
4.3%
b18
 
3.7%
-17
 
3.5%
Other values (23)174
35.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)490
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
107
21.8%
s30
 
6.1%
229
 
5.9%
127
 
5.5%
o24
 
4.9%
522
 
4.5%
g21
 
4.3%
021
 
4.3%
b18
 
3.7%
-17
 
3.5%
Other values (23)174
35.5%

discontinued
Real number (ℝ)

Constant  Zeros 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros45
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size533.0 B
2025-10-30T18:47:38.755334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics
Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing

2025-10-30T18:47:38.835841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
045
100.0%
ValueCountFrequency (%)
045
100.0%
ValueCountFrequency (%)
045
100.0%

minimum_reorder_quantity
Real number (ℝ)

Missing 

Distinct6
Distinct (%)20.0%
Missing15
Missing (%)33.3%
Infinite0
Infinite (%)0.0%
Mean15
Minimum5
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size533.0 B
2025-10-30T18:47:38.910211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum5
5-th percentile5
Q110
median10
Q325
95-th percentile27.75
Maximum30
Range25
Interquartile range (IQR)15

Descriptive statistics
Standard deviation8.304547985
Coefficient of variation (CV)0.5536365324
Kurtosis-1.286011905
Mean15
Median Absolute Deviation (MAD)5
Skewness0.4838140679
Sum450
Variance68.96551724
MonotonicityIncreasing

2025-10-30T18:47:39.015641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1012
26.7%
257
15.6%
55
 
11.1%
152
 
4.4%
202
 
4.4%
302
 
4.4%
(Missing)15
33.3%
ValueCountFrequency (%)
55
11.1%
1012
26.7%
152
 
4.4%
202
 
4.4%
257
15.6%
ValueCountFrequency (%)
302
 
4.4%
257
15.6%
202
 
4.4%
152
 
4.4%
1012
26.7%
Distinct16
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2025-10-30T18:47:39.149591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length31
Median length18
Mean length13.71111111
Min length3

Characters and Unicode
Total characters617
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique5 ?
Unique (%)11.1%

Sample
1st rowCanned Fruit & Vegetables
2nd rowCanned Fruit & Vegetables
3rd rowCanned Fruit & Vegetables
4th rowCanned Fruit & Vegetables
5th rowCanned Fruit & Vegetables

ValueCountFrequency (%)
17
16.5%
fruit13
12.6%
canned11
10.7%
vegetables8
 
7.8%
beverages5
 
4.9%
dried5
 
4.9%
nuts5
 
4.9%
baked4
 
3.9%
goods4
 
3.9%
mixes4
 
3.9%
Other values (15)27
26.2%
2025-10-30T18:47:39.502687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e82
13.3%
58
 
9.4%
s47
 
7.6%
a46
 
7.5%
t36
 
5.8%
n33
 
5.3%
r33
 
5.3%
d31
 
5.0%
i30
 
4.9%
u25
 
4.1%
Other values (30)196
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)617
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e82
13.3%
58
 
9.4%
s47
 
7.6%
a46
 
7.5%
t36
 
5.8%
n33
 
5.3%
r33
 
5.3%
d31
 
5.0%
i30
 
4.9%
u25
 
4.1%
Other values (30)196
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)617
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e82
13.3%
58
 
9.4%
s47
 
7.6%
a46
 
7.5%
t36
 
5.8%
n33
 
5.3%
r33
 
5.3%
d31
 
5.0%
i30
 
4.9%
u25
 
4.1%
Other values (30)196
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)617
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e82
13.3%
58
 
9.4%
s47
 
7.6%
a46
 
7.5%
t36
 
5.8%
n33
 
5.3%
r33
 
5.3%
d31
 
5.0%
i30
 
4.9%
u25
 
4.1%
Other values (30)196
31.8%

attachments
Text

Constant 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size2.6 KiB

Length
Max length0
Median length0
Mean length0
Min length0

Characters and Unicode
Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st row
2nd row
3rd row
4th row
5th row

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

__table_name__
Text

Constant 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2025-10-30T18:47:39.747565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length8
Median length8
Mean length8
Min length8

Characters and Unicode
Total characters360
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowproducts
2nd rowproducts
3rd rowproducts
4th rowproducts
5th rowproducts

ValueCountFrequency (%)
products45
100.0%
2025-10-30T18:47:39.996265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
p45
12.5%
r45
12.5%
o45
12.5%
d45
12.5%
u45
12.5%
c45
12.5%
t45
12.5%
s45
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)360
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p45
12.5%
r45
12.5%
o45
12.5%
d45
12.5%
u45
12.5%
c45
12.5%
t45
12.5%
s45
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)360
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p45
12.5%
r45
12.5%
o45
12.5%
d45
12.5%
u45
12.5%
c45
12.5%
t45
12.5%
s45
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)360
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p45
12.5%
r45
12.5%
o45
12.5%
d45
12.5%
u45
12.5%
c45
12.5%
t45
12.5%
s45
12.5%

Report generated by YData.


Table: purchase_order_details

Profile: purchase_order_details

Overview

Brought to you by YData

Dataset statistics
Number of variables9
Number of observations55
Missing cells24
Missing cells (%)4.8%
Total size in memory8.1 KiB
Average record size in memory150.3 B

Variable types
Numeric7
DateTime1
Text1

Alerts

__table_name__ has constant value "purchase_order_details"Constant
date_received has 12 (21.8%) missing valuesMissing
inventory_id has 12 (21.8%) missing valuesMissing
id has unique valuesUnique
posted_to_inventory has 12 (21.8%) zerosZeros

Reproduction
Analysis started2025-10-30 16:47:41.317092
Analysis finished2025-10-30 16:47:41.402079
Duration0.08 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean265.3636364
Minimum238
Maximum295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size623.0 B
2025-10-30T18:47:41.503587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum238
5-th percentile240.7
Q1251.5
median265
Q3278.5
95-th percentile292.3
Maximum295
Range57
Interquartile range (IQR)27

Descriptive statistics
Standard deviation16.58687973
Coefficient of variation (CV)0.06250622714
Kurtosis-1.109720524
Mean265.3636364
Median Absolute Deviation (MAD)14
Skewness0.09284713327
Sum14595
Variance275.1245791
MonotonicityNot monotonic

2025-10-30T18:47:41.682866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2381
 
1.8%
2411
 
1.8%
2401
 
1.8%
2431
 
1.8%
2441
 
1.8%
2391
 
1.8%
2421
 
1.8%
2901
 
1.8%
2881
 
1.8%
2891
 
1.8%
Other values (45)45
81.8%
ValueCountFrequency (%)
2381
1.8%
2391
1.8%
2401
1.8%
2411
1.8%
2421
1.8%
ValueCountFrequency (%)
2951
1.8%
2941
1.8%
2931
1.8%
2921
1.8%
2901
1.8%

purchase_order_id
Real number (ℝ)

Distinct28
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.8363636
Minimum90
Maximum148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size623.0 B
2025-10-30T18:47:41.824754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum90
5-th percentile90
Q192
median93
Q3104.5
95-th percentile146
Maximum148
Range58
Interquartile range (IQR)12.5

Descriptive statistics
Standard deviation17.41408927
Coefficient of variation (CV)0.171000698
Kurtosis2.17396244
Mean101.8363636
Median Absolute Deviation (MAD)3
Skewness1.858271259
Sum5601
Variance303.2505051
MonotonicityNot monotonic

2025-10-30T18:47:41.925299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
9215
27.3%
917
 
12.7%
905
 
9.1%
933
 
5.5%
1462
 
3.6%
1421
 
1.8%
1031
 
1.8%
1401
 
1.8%
1081
 
1.8%
1091
 
1.8%
Other values (18)18
32.7%
ValueCountFrequency (%)
905
 
9.1%
917
12.7%
9215
27.3%
933
 
5.5%
941
 
1.8%
ValueCountFrequency (%)
1481
1.8%
1471
1.8%
1462
3.6%
1421
1.8%
1411
1.8%

product_id
Real number (ℝ)

Distinct29
Distinct (%)52.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.36363636
Minimum1
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size623.0 B
2025-10-30T18:47:42.060101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum1
5-th percentile2.4
Q115.5
median40
Q354
95-th percentile80.3
Maximum85
Range84
Interquartile range (IQR)38.5

Descriptive statistics
Standard deviation25.75767677
Coefficient of variation (CV)0.7083361111
Kurtosis-1.10546677
Mean36.36363636
Median Absolute Deviation (MAD)21
Skewness0.2609547134
Sum2000
Variance663.4579125
MonotonicityNot monotonic

2025-10-30T18:47:42.339837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
195
 
9.1%
434
 
7.3%
344
 
7.3%
723
 
5.5%
13
 
5.5%
413
 
5.5%
512
 
3.6%
62
 
3.6%
482
 
3.6%
202
 
3.6%
Other values (19)25
45.5%
ValueCountFrequency (%)
13
5.5%
32
3.6%
42
3.6%
51
 
1.8%
62
3.6%
ValueCountFrequency (%)
851
1.8%
812
3.6%
801
1.8%
771
1.8%
741
1.8%

quantity
Real number (ℝ)

Distinct16
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.47272727
Minimum1
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size568.0 B
2025-10-30T18:47:42.515046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum1
5-th percentile10
Q140
median40
Q3100
95-th percentile265
Maximum300
Range299
Interquartile range (IQR)60

Descriptive statistics
Standard deviation73.04104745
Coefficient of variation (CV)0.9427969044
Kurtosis3.301769212
Mean77.47272727
Median Absolute Deviation (MAD)20
Skewness1.912953888
Sum4261
Variance5334.994613
MonotonicityNot monotonic

2025-10-30T18:47:42.766132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
4017
30.9%
1008
14.5%
504
 
7.3%
204
 
7.3%
103
 
5.5%
1203
 
5.5%
3003
 
5.5%
252
 
3.6%
2002
 
3.6%
602
 
3.6%
Other values (6)7
12.7%
ValueCountFrequency (%)
11
 
1.8%
103
5.5%
204
7.3%
252
3.6%
301
 
1.8%
ValueCountFrequency (%)
3003
5.5%
2501
 
1.8%
2002
3.6%
1251
 
1.8%
1203
5.5%

unit_cost
Real number (ℝ)

Distinct25
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.49545455
Minimum2
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size568.0 B
2025-10-30T18:47:43.019678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum2
5-th percentile4.4
Q18
median14
Q326
95-th percentile39.3
Maximum61
Range59
Interquartile range (IQR)18

Descriptive statistics
Standard deviation13.06877206
Coefficient of variation (CV)0.7469809956
Kurtosis2.537306376
Mean17.49545455
Median Absolute Deviation (MAD)7
Skewness1.53165381
Sum962.25
Variance170.792803
MonotonicityNot monotonic

2025-10-30T18:47:43.214633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
78
14.5%
107
12.7%
344
 
7.3%
84
 
7.3%
164
 
7.3%
263
 
5.5%
143
 
5.5%
302
 
3.6%
152
 
3.6%
22
 
3.6%
Other values (15)16
29.1%
ValueCountFrequency (%)
22
 
3.6%
31
 
1.8%
51
 
1.8%
78
14.5%
84
7.3%
ValueCountFrequency (%)
611
 
1.8%
601
 
1.8%
401
 
1.8%
391
 
1.8%
344
7.3%

date_received
Date

Missing 

Distinct6
Distinct (%)14.0%
Missing12
Missing (%)21.8%
Memory size568.0 B
Minimum2006-01-22 00:00:00
Maximum2006-04-17 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-30T18:47:43.323252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:47:43.447326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)

posted_to_inventory
Real number (ℝ)

Zeros 

Distinct2
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7818181818
Minimum0
Maximum1
Zeros12
Zeros (%)21.8%
Negative0
Negative (%)0.0%
Memory size623.0 B
2025-10-30T18:47:43.531337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics
Standard deviation0.4168181543
Coefficient of variation (CV)0.5331394997
Kurtosis-0.03341546562
Mean0.7818181818
Median Absolute Deviation (MAD)0
Skewness-1.4032634
Sum43
Variance0.1737373737
MonotonicityNot monotonic

2025-10-30T18:47:43.640593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
143
78.2%
012
 
21.8%
ValueCountFrequency (%)
012
 
21.8%
143
78.2%
ValueCountFrequency (%)
143
78.2%
012
 
21.8%

inventory_id
Real number (ℝ)

Missing 

Distinct43
Distinct (%)100.0%
Missing12
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean64.76744186
Minimum35
Maximum115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size623.0 B
2025-10-30T18:47:43.767836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum35
5-th percentile37.1
Q145.5
median56
Q379
95-th percentile110.8
Maximum115
Range80
Interquartile range (IQR)33.5

Descriptive statistics
Standard deviation25.18487567
Coefficient of variation (CV)0.3888508631
Kurtosis-0.7107985776
Mean64.76744186
Median Absolute Deviation (MAD)15
Skewness0.8126450747
Sum2785
Variance634.2779623
MonotonicityNot monotonic

2025-10-30T18:47:44.560280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
591
 
1.8%
551
 
1.8%
561
 
1.8%
421
 
1.8%
541
 
1.8%
401
 
1.8%
1111
 
1.8%
451
 
1.8%
471
 
1.8%
521
 
1.8%
Other values (33)33
60.0%
(Missing)12
 
21.8%
ValueCountFrequency (%)
351
1.8%
361
1.8%
371
1.8%
381
1.8%
391
1.8%
ValueCountFrequency (%)
1151
1.8%
1131
1.8%
1111
1.8%
1091
1.8%
1071
1.8%

__table_name__
Text

Constant 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2025-10-30T18:47:44.952053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length22
Median length22
Mean length22
Min length22

Characters and Unicode
Total characters1210
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowpurchase_order_details
2nd rowpurchase_order_details
3rd rowpurchase_order_details
4th rowpurchase_order_details
5th rowpurchase_order_details

ValueCountFrequency (%)
purchase_order_details55
100.0%
2025-10-30T18:47:45.207311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r165
13.6%
e165
13.6%
d110
9.1%
_110
9.1%
s110
9.1%
a110
9.1%
h55
 
4.5%
c55
 
4.5%
p55
 
4.5%
u55
 
4.5%
Other values (4)220
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)1210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r165
13.6%
e165
13.6%
d110
9.1%
_110
9.1%
s110
9.1%
a110
9.1%
h55
 
4.5%
c55
 
4.5%
p55
 
4.5%
u55
 
4.5%
Other values (4)220
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r165
13.6%
e165
13.6%
d110
9.1%
_110
9.1%
s110
9.1%
a110
9.1%
h55
 
4.5%
c55
 
4.5%
p55
 
4.5%
u55
 
4.5%
Other values (4)220
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r165
13.6%
e165
13.6%
d110
9.1%
_110
9.1%
s110
9.1%
a110
9.1%
h55
 
4.5%
c55
 
4.5%
p55
 
4.5%
u55
 
4.5%
Other values (4)220
18.2%

Report generated by YData.


Table: purchase_order_status

Profile: purchase_order_status

Overview

Brought to you by YData

Dataset statistics
Number of variables3
Number of observations4
Missing cells0
Missing cells (%)0.0%
Total size in memory730.0 B
Average record size in memory182.5 B

Variable types
Numeric1
Text2

Alerts

__table_name__ has constant value "purchase_order_status"Constant
id has unique valuesUnique
status has unique valuesUnique
id has 1 (25.0%) zerosZeros

Reproduction
Analysis started2025-10-30 16:47:46.377800
Analysis finished2025-10-30 16:47:46.418535
Duration0.04 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique  Zeros 

Distinct4
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5
Minimum0
Maximum3
Zeros1
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size164.0 B
2025-10-30T18:47:46.467381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile0.15
Q10.75
median1.5
Q32.25
95-th percentile2.85
Maximum3
Range3
Interquartile range (IQR)1.5

Descriptive statistics
Standard deviation1.290994449
Coefficient of variation (CV)0.8606629658
Kurtosis-1.2
Mean1.5
Median Absolute Deviation (MAD)1
Skewness0
Sum6
Variance1.666666667
MonotonicityStrictly increasing

2025-10-30T18:47:46.703548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
01
25.0%
11
25.0%
21
25.0%
31
25.0%
ValueCountFrequency (%)
01
25.0%
11
25.0%
21
25.0%
31
25.0%
ValueCountFrequency (%)
31
25.0%
21
25.0%
11
25.0%
01
25.0%

status
Text

Unique 

Distinct4
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size382.0 B
2025-10-30T18:47:46.838269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length9
Median length7
Mean length6.5
Min length3

Characters and Unicode
Total characters26
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique4 ?
Unique (%)100.0%

Sample
1st rowNew
2nd rowSubmitted
3rd rowApproved
4th rowClosed

ValueCountFrequency (%)
new1
25.0%
submitted1
25.0%
approved1
25.0%
closed1
25.0%
2025-10-30T18:47:47.024960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e4
15.4%
d3
 
11.5%
o2
 
7.7%
t2
 
7.7%
p2
 
7.7%
N1
 
3.8%
b1
 
3.8%
u1
 
3.8%
w1
 
3.8%
S1
 
3.8%
Other values (8)8
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)26
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e4
15.4%
d3
 
11.5%
o2
 
7.7%
t2
 
7.7%
p2
 
7.7%
N1
 
3.8%
b1
 
3.8%
u1
 
3.8%
w1
 
3.8%
S1
 
3.8%
Other values (8)8
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)26
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e4
15.4%
d3
 
11.5%
o2
 
7.7%
t2
 
7.7%
p2
 
7.7%
N1
 
3.8%
b1
 
3.8%
u1
 
3.8%
w1
 
3.8%
S1
 
3.8%
Other values (8)8
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)26
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e4
15.4%
d3
 
11.5%
o2
 
7.7%
t2
 
7.7%
p2
 
7.7%
N1
 
3.8%
b1
 
3.8%
u1
 
3.8%
w1
 
3.8%
S1
 
3.8%
Other values (8)8
30.8%

__table_name__
Text

Constant 

Distinct1
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size440.0 B
2025-10-30T18:47:47.144081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length21
Median length21
Mean length21
Min length21

Characters and Unicode
Total characters84
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowpurchase_order_status
2nd rowpurchase_order_status
3rd rowpurchase_order_status
4th rowpurchase_order_status

ValueCountFrequency (%)
purchase_order_status4
100.0%
2025-10-30T18:47:47.364854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r12
14.3%
s12
14.3%
a8
9.5%
u8
9.5%
t8
9.5%
_8
9.5%
e8
9.5%
p4
 
4.8%
c4
 
4.8%
h4
 
4.8%
Other values (2)8
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)84
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r12
14.3%
s12
14.3%
a8
9.5%
u8
9.5%
t8
9.5%
_8
9.5%
e8
9.5%
p4
 
4.8%
c4
 
4.8%
h4
 
4.8%
Other values (2)8
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)84
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r12
14.3%
s12
14.3%
a8
9.5%
u8
9.5%
t8
9.5%
_8
9.5%
e8
9.5%
p4
 
4.8%
c4
 
4.8%
h4
 
4.8%
Other values (2)8
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)84
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r12
14.3%
s12
14.3%
a8
9.5%
u8
9.5%
t8
9.5%
_8
9.5%
e8
9.5%
p4
 
4.8%
c4
 
4.8%
h4
 
4.8%
Other values (2)8
9.5%

Report generated by YData.


Table: purchase_orders

Profile: purchase_orders

Overview

Brought to you by YData

Dataset statistics
Number of variables17
Number of observations28
Missing cells103
Missing cells (%)21.6%
Total size in memory7.8 KiB
Average record size in memory285.3 B

Variable types
Numeric9
DateTime3
Unsupported2
Text3

Alerts

shipping_fee has constant value "0.0"Constant
taxes has constant value "0.0"Constant
payment_amount has constant value "0.0"Constant
payment_method has constant value "Check"Constant
approved_by has constant value "2"Constant
__table_name__ has constant value "purchase_orders"Constant
created_by has 3 (10.7%) missing valuesMissing
expected_date has 28 (100.0%) missing valuesMissing
payment_date has 28 (100.0%) missing valuesMissing
payment_method has 26 (92.9%) missing valuesMissing
notes has 12 (42.9%) missing valuesMissing
approved_by has 3 (10.7%) missing valuesMissing
approved_date has 3 (10.7%) missing valuesMissing
id has unique valuesUnique
expected_date is an unsupported type, check if it needs cleaning or further analysisUnsupported
payment_date is an unsupported type, check if it needs cleaning or further analysisUnsupported
shipping_fee has 28 (100.0%) zerosZeros
taxes has 28 (100.0%) zerosZeros
payment_amount has 28 (100.0%) zerosZeros

Reproduction
Analysis started2025-10-30 16:47:48.236516
Analysis finished2025-10-30 16:47:48.366463
Duration0.13 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.8214286
Minimum90
Maximum148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2025-10-30T18:47:48.448255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum90
5-th percentile91.35
Q196.75
median103.5
Q3110.25
95-th percentile146.65
Maximum148
Range58
Interquartile range (IQR)13.5

Descriptive statistics
Standard deviation19.11379872
Coefficient of variation (CV)0.1740443461
Kurtosis-0.1453706217
Mean109.8214286
Median Absolute Deviation (MAD)7
Skewness1.159088306
Sum3075
Variance365.3373016
MonotonicityNot monotonic

2025-10-30T18:47:48.856336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
901
 
3.6%
931
 
3.6%
951
 
3.6%
911
 
3.6%
921
 
3.6%
941
 
3.6%
961
 
3.6%
971
 
3.6%
981
 
3.6%
991
 
3.6%
Other values (18)18
64.3%
ValueCountFrequency (%)
901
3.6%
911
3.6%
921
3.6%
931
3.6%
941
3.6%
ValueCountFrequency (%)
1481
3.6%
1471
3.6%
1461
3.6%
1421
3.6%
1411
3.6%

supplier_id
Real number (ℝ)

Distinct8
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.178571429
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2025-10-30T18:47:49.180777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile7.65
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics
Standard deviation2.326236373
Coefficient of variation (CV)0.7318496453
Kurtosis-0.6545868539
Mean3.178571429
Median Absolute Deviation (MAD)1
Skewness0.8509716215
Sum89
Variance5.411375661
MonotonicityNot monotonic

2025-10-30T18:47:49.296916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
29
32.1%
18
28.6%
63
 
10.7%
53
 
10.7%
82
 
7.1%
41
 
3.6%
31
 
3.6%
71
 
3.6%
ValueCountFrequency (%)
18
28.6%
29
32.1%
31
 
3.6%
41
 
3.6%
53
 
10.7%
ValueCountFrequency (%)
82
7.1%
71
 
3.6%
63
10.7%
53
10.7%
41
 
3.6%

created_by
Real number (ℝ)

Missing 

Distinct8
Distinct (%)32.0%
Missing3
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean3.36
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2025-10-30T18:47:49.441267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile7
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics
Standard deviation2.23383079
Coefficient of variation (CV)0.6648305924
Kurtosis0.2941509212
Mean3.36
Median Absolute Deviation (MAD)1
Skewness1.13965088
Sum84
Variance4.99
MonotonicityNot monotonic

2025-10-30T18:47:49.590202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
211
39.3%
73
 
10.7%
43
 
10.7%
13
 
10.7%
32
 
7.1%
51
 
3.6%
91
 
3.6%
61
 
3.6%
(Missing)3
 
10.7%
ValueCountFrequency (%)
13
 
10.7%
211
39.3%
32
 
7.1%
43
 
10.7%
51
 
3.6%
ValueCountFrequency (%)
91
 
3.6%
73
10.7%
61
 
3.6%
51
 
3.6%
43
10.7%
Distinct7
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size352.0 B
Minimum2006-01-14 00:00:00
Maximum2006-04-26 18:33:52
Invalid dates0
Invalid dates (%)0.0%
2025-10-30T18:47:49.697536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:47:49.826999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
Distinct9
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Memory size352.0 B
Minimum2006-01-22 00:00:00
Maximum2006-04-26 18:33:52
Invalid dates0
Invalid dates (%)0.0%
2025-10-30T18:47:49.939075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:47:50.062180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)

status_id
Real number (ℝ)

Distinct2
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.892857143
Minimum1
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2025-10-30T18:47:50.138272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile2
Maximum2
Range1
Interquartile range (IQR)0

Descriptive statistics
Standard deviation0.3149703942
Coefficient of variation (CV)0.1663994535
Kurtosis5.613784615
Mean1.892857143
Median Absolute Deviation (MAD)0
Skewness-2.686455177
Sum53
Variance0.09920634921
MonotonicityDecreasing

2025-10-30T18:47:50.196521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
225
89.3%
13
 
10.7%
ValueCountFrequency (%)
13
 
10.7%
225
89.3%
ValueCountFrequency (%)
225
89.3%
13
 
10.7%

expected_date
Unsupported

Missing  Rejected  Unsupported 

Missing28
Missing (%)100.0%
Memory size352.0 B

shipping_fee
Real number (ℝ)

Constant  Zeros 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros28
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size352.0 B
2025-10-30T18:47:50.264038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics
Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing

2025-10-30T18:47:50.322531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
028
100.0%
ValueCountFrequency (%)
028
100.0%
ValueCountFrequency (%)
028
100.0%

taxes
Real number (ℝ)

Constant  Zeros 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros28
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size352.0 B
2025-10-30T18:47:50.570406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics
Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing

2025-10-30T18:47:50.672679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
028
100.0%
ValueCountFrequency (%)
028
100.0%
ValueCountFrequency (%)
028
100.0%

payment_date
Unsupported

Missing  Rejected  Unsupported 

Missing28
Missing (%)100.0%
Memory size352.0 B

payment_amount
Real number (ℝ)

Constant  Zeros 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros28
Zeros (%)100.0%
Negative0
Negative (%)0.0%
Memory size352.0 B
2025-10-30T18:47:50.755789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics
Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing

2025-10-30T18:47:50.843347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
028
100.0%
ValueCountFrequency (%)
028
100.0%
ValueCountFrequency (%)
028
100.0%

payment_method
Text

Constant  Missing 

Distinct1
Distinct (%)50.0%
Missing26
Missing (%)92.9%
Memory size1.1 KiB
2025-10-30T18:47:50.933758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length5
Median length5
Mean length5
Min length5

Characters and Unicode
Total characters10
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowCheck
2nd rowCheck

ValueCountFrequency (%)
check2
100.0%
2025-10-30T18:47:51.057873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C2
20.0%
h2
20.0%
e2
20.0%
c2
20.0%
k2
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)10
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C2
20.0%
h2
20.0%
e2
20.0%
c2
20.0%
k2
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C2
20.0%
h2
20.0%
e2
20.0%
c2
20.0%
k2
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C2
20.0%
h2
20.0%
e2
20.0%
c2
20.0%
k2
20.0%

notes
Text

Missing 

Distinct14
Distinct (%)87.5%
Missing12
Missing (%)42.9%
Memory size2.0 KiB
2025-10-30T18:47:51.167857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length37
Median length37
Mean length37
Min length37

Characters and Unicode
Total characters592
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique12 ?
Unique (%)75.0%

Sample
1st rowPurchase generated based on Order #30
2nd rowPurchase generated based on Order #33
3rd rowPurchase generated based on Order #36
4th rowPurchase generated based on Order #38
5th rowPurchase generated based on Order #39

ValueCountFrequency (%)
purchase16
16.7%
generated16
16.7%
based16
16.7%
on16
16.7%
order16
16.7%
482
 
2.1%
462
 
2.1%
361
 
1.0%
381
 
1.0%
301
 
1.0%
Other values (9)9
9.4%
2025-10-30T18:47:51.386186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e96
16.2%
80
13.5%
r64
10.8%
a48
 
8.1%
d48
 
8.1%
n32
 
5.4%
s32
 
5.4%
u16
 
2.7%
h16
 
2.7%
c16
 
2.7%
Other values (17)144
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)592
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e96
16.2%
80
13.5%
r64
10.8%
a48
 
8.1%
d48
 
8.1%
n32
 
5.4%
s32
 
5.4%
u16
 
2.7%
h16
 
2.7%
c16
 
2.7%
Other values (17)144
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)592
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e96
16.2%
80
13.5%
r64
10.8%
a48
 
8.1%
d48
 
8.1%
n32
 
5.4%
s32
 
5.4%
u16
 
2.7%
h16
 
2.7%
c16
 
2.7%
Other values (17)144
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)592
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e96
16.2%
80
13.5%
r64
10.8%
a48
 
8.1%
d48
 
8.1%
n32
 
5.4%
s32
 
5.4%
u16
 
2.7%
h16
 
2.7%
c16
 
2.7%
Other values (17)144
24.3%

approved_by
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)4.0%
Missing3
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean2
Minimum2
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2025-10-30T18:47:51.455317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum2
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum2
Range0
Interquartile range (IQR)0

Descriptive statistics
Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean2
Median Absolute Deviation (MAD)0
Skewness0
Sum50
Variance0
MonotonicityIncreasing

2025-10-30T18:47:51.536835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
225
89.3%
(Missing)3
 
10.7%
ValueCountFrequency (%)
225
89.3%
ValueCountFrequency (%)
225
89.3%

approved_date
Date

Missing 

Distinct5
Distinct (%)20.0%
Missing3
Missing (%)10.7%
Memory size352.0 B
Minimum2006-01-22 00:00:00
Maximum2006-04-25 17:18:51
Invalid dates0
Invalid dates (%)0.0%
2025-10-30T18:47:51.632399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-30T18:47:51.744875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)

submitted_by
Real number (ℝ)

Distinct8
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.214285714
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2025-10-30T18:47:51.853153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile7
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics
Standard deviation2.149196971
Coefficient of variation (CV)0.6686390576
Kurtosis0.7791433734
Mean3.214285714
Median Absolute Deviation (MAD)0.5
Skewness1.316046446
Sum90
Variance4.619047619
MonotonicityNot monotonic

2025-10-30T18:47:51.963742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
214
50.0%
73
 
10.7%
43
 
10.7%
13
 
10.7%
32
 
7.1%
51
 
3.6%
91
 
3.6%
61
 
3.6%
ValueCountFrequency (%)
13
 
10.7%
214
50.0%
32
 
7.1%
43
 
10.7%
51
 
3.6%
ValueCountFrequency (%)
91
 
3.6%
73
10.7%
61
 
3.6%
51
 
3.6%
43
10.7%

__table_name__
Text

Constant 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2025-10-30T18:47:52.066482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length15
Median length15
Mean length15
Min length15

Characters and Unicode
Total characters420
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowpurchase_orders
2nd rowpurchase_orders
3rd rowpurchase_orders
4th rowpurchase_orders
5th rowpurchase_orders

ValueCountFrequency (%)
purchase_orders28
100.0%
2025-10-30T18:47:52.251746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r84
20.0%
e56
13.3%
s56
13.3%
u28
 
6.7%
p28
 
6.7%
h28
 
6.7%
c28
 
6.7%
a28
 
6.7%
_28
 
6.7%
o28
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)420
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r84
20.0%
e56
13.3%
s56
13.3%
u28
 
6.7%
p28
 
6.7%
h28
 
6.7%
c28
 
6.7%
a28
 
6.7%
_28
 
6.7%
o28
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)420
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r84
20.0%
e56
13.3%
s56
13.3%
u28
 
6.7%
p28
 
6.7%
h28
 
6.7%
c28
 
6.7%
a28
 
6.7%
_28
 
6.7%
o28
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)420
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r84
20.0%
e56
13.3%
s56
13.3%
u28
 
6.7%
p28
 
6.7%
h28
 
6.7%
c28
 
6.7%
a28
 
6.7%
_28
 
6.7%
o28
 
6.7%

Report generated by YData.


Table: sales_reports

Profile: sales_reports

Overview

Brought to you by YData

Dataset statistics
Number of variables6
Number of observations5
Missing cells0
Missing cells (%)0.0%
Total size in memory2.5 KiB
Average record size in memory502.8 B

Variable types
Text6

Alerts

__table_name__ has constant value "sales_reports"Constant
group_by has unique valuesUnique
display has unique valuesUnique
title has unique valuesUnique
filter_row_source has unique valuesUnique

Reproduction
Analysis started2025-10-30 16:47:53.189634
Analysis finished2025-10-30 16:47:53.273064
Duration0.08 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

group_by
Text

Unique 

Distinct5
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size467.0 B
2025-10-30T18:47:53.334069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length14
Median length11
Mean length10.8
Min length8

Characters and Unicode
Total characters54
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique5 ?
Unique (%)100.0%

Sample
1st rowcountry_region
2nd rowCustomer ID
3rd rowemployee_id
4th rowCategory
5th rowProduct ID

ValueCountFrequency (%)
id2
28.6%
country_region1
14.3%
customer1
14.3%
employee_id1
14.3%
category1
14.3%
product1
14.3%
2025-10-30T18:47:53.541325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o6
 
11.1%
e6
 
11.1%
r5
 
9.3%
t4
 
7.4%
y3
 
5.6%
u3
 
5.6%
n2
 
3.7%
c2
 
3.7%
_2
 
3.7%
g2
 
3.7%
Other values (12)19
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)54
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o6
 
11.1%
e6
 
11.1%
r5
 
9.3%
t4
 
7.4%
y3
 
5.6%
u3
 
5.6%
n2
 
3.7%
c2
 
3.7%
_2
 
3.7%
g2
 
3.7%
Other values (12)19
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)54
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o6
 
11.1%
e6
 
11.1%
r5
 
9.3%
t4
 
7.4%
y3
 
5.6%
u3
 
5.6%
n2
 
3.7%
c2
 
3.7%
_2
 
3.7%
g2
 
3.7%
Other values (12)19
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)54
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o6
 
11.1%
e6
 
11.1%
r5
 
9.3%
t4
 
7.4%
y3
 
5.6%
u3
 
5.6%
n2
 
3.7%
c2
 
3.7%
_2
 
3.7%
g2
 
3.7%
Other values (12)19
35.2%

display
Text

Unique 

Distinct5
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size458.0 B
2025-10-30T18:47:53.641560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length14
Median length8
Mean length9
Min length7

Characters and Unicode
Total characters45
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique5 ?
Unique (%)100.0%

Sample
1st rowCountry/Region
2nd rowCustomer
3rd rowEmployee
4th rowCategory
5th rowProduct

ValueCountFrequency (%)
country/region1
20.0%
customer1
20.0%
employee1
20.0%
category1
20.0%
product1
20.0%
2025-10-30T18:47:53.860762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o6
13.3%
e5
11.1%
t4
 
8.9%
r4
 
8.9%
C3
 
6.7%
y3
 
6.7%
u3
 
6.7%
n2
 
4.4%
g2
 
4.4%
m2
 
4.4%
Other values (11)11
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)45
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o6
13.3%
e5
11.1%
t4
 
8.9%
r4
 
8.9%
C3
 
6.7%
y3
 
6.7%
u3
 
6.7%
n2
 
4.4%
g2
 
4.4%
m2
 
4.4%
Other values (11)11
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o6
13.3%
e5
11.1%
t4
 
8.9%
r4
 
8.9%
C3
 
6.7%
y3
 
6.7%
u3
 
6.7%
n2
 
4.4%
g2
 
4.4%
m2
 
4.4%
Other values (11)11
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o6
13.3%
e5
11.1%
t4
 
8.9%
r4
 
8.9%
C3
 
6.7%
y3
 
6.7%
u3
 
6.7%
n2
 
4.4%
g2
 
4.4%
m2
 
4.4%
Other values (11)11
24.4%

title
Text

Unique 

Distinct5
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size496.0 B
2025-10-30T18:47:53.937255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length17
Median length17
Mean length16.6
Min length16

Characters and Unicode
Total characters83
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique5 ?
Unique (%)100.0%

Sample
1st rowSales By Country
2nd rowSales By Customer
3rd rowSales By Employee
4th rowSales By Category
5th rowSales by Product

ValueCountFrequency (%)
sales5
33.3%
by5
33.3%
country1
 
6.7%
customer1
 
6.7%
employee1
 
6.7%
category1
 
6.7%
product1
 
6.7%
2025-10-30T18:47:54.121654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10
12.0%
e9
10.8%
y8
 
9.6%
l6
 
7.2%
s6
 
7.2%
a6
 
7.2%
S5
 
6.0%
o5
 
6.0%
B4
 
4.8%
r4
 
4.8%
Other values (12)20
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)83
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
10
12.0%
e9
10.8%
y8
 
9.6%
l6
 
7.2%
s6
 
7.2%
a6
 
7.2%
S5
 
6.0%
o5
 
6.0%
B4
 
4.8%
r4
 
4.8%
Other values (12)20
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)83
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
10
12.0%
e9
10.8%
y8
 
9.6%
l6
 
7.2%
s6
 
7.2%
a6
 
7.2%
S5
 
6.0%
o5
 
6.0%
B4
 
4.8%
r4
 
4.8%
Other values (12)20
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)83
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
10
12.0%
e9
10.8%
y8
 
9.6%
l6
 
7.2%
s6
 
7.2%
a6
 
7.2%
S5
 
6.0%
o5
 
6.0%
B4
 
4.8%
r4
 
4.8%
Other values (12)20
24.1%

filter_row_source
Text

Unique 

Distinct5
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size837.0 B
2025-10-30T18:47:54.201197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length100
Median length85
Mean length84.8
Min length71

Characters and Unicode
Total characters424
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique5 ?
Unique (%)100.0%

Sample
1st rowSELECT DISTINCT [country_region] FROM [customers Extended] ORDER BY [country_region];
2nd rowSELECT DISTINCT [Company] FROM [customers Extended] ORDER BY [Company];
3rd rowSELECT DISTINCT [Employee Name] FROM [`dl_northwind`.`employees` Extended] ORDER BY [Employee Name];
4th rowSELECT DISTINCT [Category] FROM [`dl_northwind`.`products`] ORDER BY [Category];
5th rowSELECT DISTINCT [Product Name] FROM [`dl_northwind`.`products`] ORDER BY [Product Name];

ValueCountFrequency (%)
select5
10.6%
distinct5
10.6%
from5
10.6%
order5
10.6%
by5
10.6%
name4
8.5%
extended3
 
6.4%
customers2
 
4.3%
country_region2
 
4.3%
company2
 
4.3%
Other values (5)9
19.1%
2025-10-30T18:47:54.405114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
42
 
9.9%
e23
 
5.4%
E20
 
4.7%
o20
 
4.7%
t16
 
3.8%
d16
 
3.8%
T15
 
3.5%
R15
 
3.5%
r15
 
3.5%
[15
 
3.5%
Other values (31)227
53.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
42
 
9.9%
e23
 
5.4%
E20
 
4.7%
o20
 
4.7%
t16
 
3.8%
d16
 
3.8%
T15
 
3.5%
R15
 
3.5%
r15
 
3.5%
[15
 
3.5%
Other values (31)227
53.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
42
 
9.9%
e23
 
5.4%
E20
 
4.7%
o20
 
4.7%
t16
 
3.8%
d16
 
3.8%
T15
 
3.5%
R15
 
3.5%
r15
 
3.5%
[15
 
3.5%
Other values (31)227
53.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
42
 
9.9%
e23
 
5.4%
E20
 
4.7%
o20
 
4.7%
t16
 
3.8%
d16
 
3.8%
T15
 
3.5%
R15
 
3.5%
r15
 
3.5%
[15
 
3.5%
Other values (31)227
53.5%

default
Text

Distinct2
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size418.0 B
2025-10-30T18:47:54.457641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length1
Median length1
Mean length1
Min length1

Characters and Unicode
Total characters5
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique1 ?
Unique (%)20.0%

Sample
1st row0
2nd row0
3rd row0
4th row0
5th row1

ValueCountFrequency (%)
04
80.0%
11
 
20.0%
2025-10-30T18:47:54.571832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
04
80.0%
11
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)5
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
04
80.0%
11
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
04
80.0%
11
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
04
80.0%
11
 
20.0%

__table_name__
Text

Constant 

Distinct1
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size478.0 B
2025-10-30T18:47:54.644300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length13
Median length13
Mean length13
Min length13

Characters and Unicode
Total characters65
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowsales_reports
2nd rowsales_reports
3rd rowsales_reports
4th rowsales_reports
5th rowsales_reports

ValueCountFrequency (%)
sales_reports5
100.0%
2025-10-30T18:47:54.815017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s15
23.1%
e10
15.4%
r10
15.4%
l5
 
7.7%
a5
 
7.7%
_5
 
7.7%
p5
 
7.7%
o5
 
7.7%
t5
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)65
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s15
23.1%
e10
15.4%
r10
15.4%
l5
 
7.7%
a5
 
7.7%
_5
 
7.7%
p5
 
7.7%
o5
 
7.7%
t5
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)65
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s15
23.1%
e10
15.4%
r10
15.4%
l5
 
7.7%
a5
 
7.7%
_5
 
7.7%
p5
 
7.7%
o5
 
7.7%
t5
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)65
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s15
23.1%
e10
15.4%
r10
15.4%
l5
 
7.7%
a5
 
7.7%
_5
 
7.7%
p5
 
7.7%
o5
 
7.7%
t5
 
7.7%

Report generated by YData.


Table: shippers

Profile: shippers

Overview

Brought to you by YData

Dataset statistics
Number of variables19
Number of observations3
Missing cells30
Missing cells (%)52.6%
Total size in memory2.4 KiB
Average record size in memory804.7 B

Variable types
Numeric1
Text8
Unsupported10

Alerts

address has constant value "123 Any Street"Constant
city has constant value "Memphis"Constant
state_province has constant value "TN"Constant
zip_postal_code has constant value "99999"Constant
country_region has constant value "USA"Constant
attachments has constant value ""Constant
__table_name__ has constant value "shippers"Constant
last_name has 3 (100.0%) missing valuesMissing
first_name has 3 (100.0%) missing valuesMissing
email_address has 3 (100.0%) missing valuesMissing
job_title has 3 (100.0%) missing valuesMissing
business_phone has 3 (100.0%) missing valuesMissing
home_phone has 3 (100.0%) missing valuesMissing
mobile_phone has 3 (100.0%) missing valuesMissing
fax_number has 3 (100.0%) missing valuesMissing
web_page has 3 (100.0%) missing valuesMissing
notes has 3 (100.0%) missing valuesMissing
id has unique valuesUnique
company has unique valuesUnique
last_name is an unsupported type, check if it needs cleaning or further analysisUnsupported
first_name is an unsupported type, check if it needs cleaning or further analysisUnsupported
email_address is an unsupported type, check if it needs cleaning or further analysisUnsupported
job_title is an unsupported type, check if it needs cleaning or further analysisUnsupported
business_phone is an unsupported type, check if it needs cleaning or further analysisUnsupported
home_phone is an unsupported type, check if it needs cleaning or further analysisUnsupported
mobile_phone is an unsupported type, check if it needs cleaning or further analysisUnsupported
fax_number is an unsupported type, check if it needs cleaning or further analysisUnsupported
web_page is an unsupported type, check if it needs cleaning or further analysisUnsupported
notes is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction
Analysis started2025-10-30 16:47:55.658889
Analysis finished2025-10-30 16:47:55.790519
Duration0.13 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct3
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size155.0 B
2025-10-30T18:47:55.849435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum1
5-th percentile1.1
Q11.5
median2
Q32.5
95-th percentile2.9
Maximum3
Range2
Interquartile range (IQR)1

Descriptive statistics
2025-10-30T18:47:55.936546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
Standard deviation1
Coefficient of variation (CV)0.5
Kurtosis
Mean2
Median Absolute Deviation (MAD)1
Skewness0
Sum6
Variance1
MonotonicityStrictly increasing
ValueCountFrequency (%)
11
33.3%
21
33.3%
31
33.3%

ValueCountFrequency (%)
11
33.3%
21
33.3%
31
33.3%
ValueCountFrequency (%)
31
33.3%
21
33.3%
11
33.3%

company
Text

Unique 

Distinct3
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size353.0 B
2025-10-30T18:47:56.019219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length18
Median length18
Mean length18
Min length18

Characters and Unicode
Total characters54
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique3 ?
Unique (%)100.0%

Sample
1st rowShipping Company A
2nd rowShipping Company B
3rd rowShipping Company C

ValueCountFrequency (%)
shipping3
33.3%
company3
33.3%
a1
 
11.1%
b1
 
11.1%
c1
 
11.1%
2025-10-30T18:47:56.202278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
p9
16.7%
i6
11.1%
n6
11.1%
6
11.1%
C4
7.4%
S3
 
5.6%
g3
 
5.6%
h3
 
5.6%
o3
 
5.6%
m3
 
5.6%
Other values (4)8
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)54
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p9
16.7%
i6
11.1%
n6
11.1%
6
11.1%
C4
7.4%
S3
 
5.6%
g3
 
5.6%
h3
 
5.6%
o3
 
5.6%
m3
 
5.6%
Other values (4)8
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)54
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p9
16.7%
i6
11.1%
n6
11.1%
6
11.1%
C4
7.4%
S3
 
5.6%
g3
 
5.6%
h3
 
5.6%
o3
 
5.6%
m3
 
5.6%
Other values (4)8
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)54
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p9
16.7%
i6
11.1%
n6
11.1%
6
11.1%
C4
7.4%
S3
 
5.6%
g3
 
5.6%
h3
 
5.6%
o3
 
5.6%
m3
 
5.6%
Other values (4)8
14.8%

last_name
Unsupported

Missing  Rejected  Unsupported 

Missing3
Missing (%)100.0%
Memory size152.0 B

first_name
Unsupported

Missing  Rejected  Unsupported 

Missing3
Missing (%)100.0%
Memory size152.0 B

email_address
Unsupported

Missing  Rejected  Unsupported 

Missing3
Missing (%)100.0%
Memory size152.0 B

job_title
Unsupported

Missing  Rejected  Unsupported 

Missing3
Missing (%)100.0%
Memory size152.0 B

business_phone
Unsupported

Missing  Rejected  Unsupported 

Missing3
Missing (%)100.0%
Memory size152.0 B

home_phone
Unsupported

Missing  Rejected  Unsupported 

Missing3
Missing (%)100.0%
Memory size152.0 B

mobile_phone
Unsupported

Missing  Rejected  Unsupported 

Missing3
Missing (%)100.0%
Memory size152.0 B

fax_number
Unsupported

Missing  Rejected  Unsupported 

Missing3
Missing (%)100.0%
Memory size152.0 B

address
Text

Constant 

Distinct1
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size341.0 B
2025-10-30T18:47:56.274149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length14
Median length14
Mean length14
Min length14

Characters and Unicode
Total characters42
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st row123 Any Street
2nd row123 Any Street
3rd row123 Any Street

ValueCountFrequency (%)
1233
33.3%
any3
33.3%
street3
33.3%
2025-10-30T18:47:56.467749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e6
14.3%
t6
14.3%
6
14.3%
33
7.1%
23
7.1%
13
7.1%
A3
7.1%
y3
7.1%
n3
7.1%
S3
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)42
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e6
14.3%
t6
14.3%
6
14.3%
33
7.1%
23
7.1%
13
7.1%
A3
7.1%
y3
7.1%
n3
7.1%
S3
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)42
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e6
14.3%
t6
14.3%
6
14.3%
33
7.1%
23
7.1%
13
7.1%
A3
7.1%
y3
7.1%
n3
7.1%
S3
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)42
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e6
14.3%
t6
14.3%
6
14.3%
33
7.1%
23
7.1%
13
7.1%
A3
7.1%
y3
7.1%
n3
7.1%
S3
7.1%

city
Text

Constant 

Distinct1
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size320.0 B
2025-10-30T18:47:56.546463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length7
Median length7
Mean length7
Min length7

Characters and Unicode
Total characters21
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowMemphis
2nd rowMemphis
3rd rowMemphis

ValueCountFrequency (%)
memphis3
100.0%
2025-10-30T18:47:56.732952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M3
14.3%
e3
14.3%
m3
14.3%
p3
14.3%
h3
14.3%
i3
14.3%
s3
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)21
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M3
14.3%
e3
14.3%
m3
14.3%
p3
14.3%
h3
14.3%
i3
14.3%
s3
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)21
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M3
14.3%
e3
14.3%
m3
14.3%
p3
14.3%
h3
14.3%
i3
14.3%
s3
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)21
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M3
14.3%
e3
14.3%
m3
14.3%
p3
14.3%
h3
14.3%
i3
14.3%
s3
14.3%

state_province
Text

Constant 

Distinct1
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size305.0 B
2025-10-30T18:47:56.775657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length2
Median length2
Mean length2
Min length2

Characters and Unicode
Total characters6
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowTN
2nd rowTN
3rd rowTN

ValueCountFrequency (%)
tn3
100.0%
2025-10-30T18:47:56.960573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T3
50.0%
N3
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T3
50.0%
N3
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T3
50.0%
N3
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T3
50.0%
N3
50.0%

zip_postal_code
Text

Constant 

Distinct1
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size314.0 B
2025-10-30T18:47:57.022455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length5
Median length5
Mean length5
Min length5

Characters and Unicode
Total characters15
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st row99999
2nd row99999
3rd row99999

ValueCountFrequency (%)
999993
100.0%
2025-10-30T18:47:57.175615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
915
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)15
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
915
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
915
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
915
100.0%

country_region
Text

Constant 

Distinct1
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size308.0 B
2025-10-30T18:47:57.219251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length3
Median length3
Mean length3
Min length3

Characters and Unicode
Total characters9
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowUSA
2nd rowUSA
3rd rowUSA

ValueCountFrequency (%)
usa3
100.0%
2025-10-30T18:47:57.515443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U3
33.3%
S3
33.3%
A3
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)9
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U3
33.3%
S3
33.3%
A3
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U3
33.3%
S3
33.3%
A3
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U3
33.3%
S3
33.3%
A3
33.3%

web_page
Unsupported

Missing  Rejected  Unsupported 

Missing3
Missing (%)100.0%
Memory size152.0 B

notes
Unsupported

Missing  Rejected  Unsupported 

Missing3
Missing (%)100.0%
Memory size152.0 B

attachments
Text

Constant 

Distinct1
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size299.0 B

Length
Max length0
Median length0
Mean length0
Min length0

Characters and Unicode
Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st row
2nd row
3rd row

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

__table_name__
Text

Constant 

Distinct1
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size323.0 B
2025-10-30T18:47:57.789690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length8
Median length8
Mean length8
Min length8

Characters and Unicode
Total characters24
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowshippers
2nd rowshippers
3rd rowshippers

ValueCountFrequency (%)
shippers3
100.0%
2025-10-30T18:47:57.973236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s6
25.0%
p6
25.0%
h3
12.5%
i3
12.5%
e3
12.5%
r3
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s6
25.0%
p6
25.0%
h3
12.5%
i3
12.5%
e3
12.5%
r3
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s6
25.0%
p6
25.0%
h3
12.5%
i3
12.5%
e3
12.5%
r3
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s6
25.0%
p6
25.0%
h3
12.5%
i3
12.5%
e3
12.5%
r3
12.5%

Report generated by YData.


Table: strings

Profile: strings

Overview

Brought to you by YData

Dataset statistics
Number of variables3
Number of observations62
Missing cells0
Missing cells (%)0.0%
Total size in memory11.3 KiB
Average record size in memory186.9 B

Variable types
Numeric1
Text2

Alerts

__table_name__ has constant value "strings"Constant
string_id has unique valuesUnique
string_data has unique valuesUnique

Reproduction
Analysis started2025-10-30 16:47:58.932528
Analysis finished2025-10-30 16:47:58.969345
Duration0.04 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

string_id
Real number (ℝ)

Unique 

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.30645161
Minimum2
Maximum114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size686.0 B
2025-10-30T18:47:59.087006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum2
5-th percentile5.05
Q117.25
median33.5
Q348.75
95-th percentile110.95
Maximum114
Range112
Interquartile range (IQR)31.5

Descriptive statistics
Standard deviation37.27900508
Coefficient of variation (CV)0.8228189089
Kurtosis-0.7600780365
Mean45.30645161
Median Absolute Deviation (MAD)16
Skewness0.872987198
Sum2809
Variance1389.72422
MonotonicityNot monotonic

2025-10-30T18:47:59.281757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41
 
1.6%
61
 
1.6%
31
 
1.6%
101
 
1.6%
51
 
1.6%
71
 
1.6%
81
 
1.6%
91
 
1.6%
21
 
1.6%
461
 
1.6%
Other values (52)52
83.9%
ValueCountFrequency (%)
21
1.6%
31
1.6%
41
1.6%
51
1.6%
61
1.6%
ValueCountFrequency (%)
1141
1.6%
1131
1.6%
1121
1.6%
1111
1.6%
1101
1.6%

string_data
Text

Unique 

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
2025-10-30T18:47:59.463676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length149
Median length73
Mean length54.82258065
Min length17

Characters and Unicode
Total characters3399
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique62 ?
Unique (%)100.0%

Sample
1st rowBack ordered product filled for Order #|
2nd rowInsufficient inventory.
3rd rowCannot remove posted inventory!
4th rowMust specify customer name!
5th rowDiscounted price below cost!

ValueCountFrequency (%)
to23
 
4.6%
order21
 
4.2%
inventory17
 
3.4%
purchase17
 
3.4%
you16
 
3.2%
product15
 
3.0%
cannot13
 
2.6%
for11
 
2.2%
must10
 
2.0%
successfully10
 
2.0%
Other values (147)350
69.6%
2025-10-30T18:47:59.768963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
451
13.3%
e343
 
10.1%
r257
 
7.6%
o249
 
7.3%
t222
 
6.5%
n194
 
5.7%
s179
 
5.3%
i166
 
4.9%
d148
 
4.4%
a147
 
4.3%
Other values (41)1043
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)3399
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
451
13.3%
e343
 
10.1%
r257
 
7.6%
o249
 
7.3%
t222
 
6.5%
n194
 
5.7%
s179
 
5.3%
i166
 
4.9%
d148
 
4.4%
a147
 
4.3%
Other values (41)1043
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3399
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
451
13.3%
e343
 
10.1%
r257
 
7.6%
o249
 
7.3%
t222
 
6.5%
n194
 
5.7%
s179
 
5.3%
i166
 
4.9%
d148
 
4.4%
a147
 
4.3%
Other values (41)1043
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3399
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
451
13.3%
e343
 
10.1%
r257
 
7.6%
o249
 
7.3%
t222
 
6.5%
n194
 
5.7%
s179
 
5.3%
i166
 
4.9%
d148
 
4.4%
a147
 
4.3%
Other values (41)1043
30.7%

__table_name__
Text

Constant 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2025-10-30T18:47:59.832475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length7
Median length7
Mean length7
Min length7

Characters and Unicode
Total characters434
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowstrings
2nd rowstrings
3rd rowstrings
4th rowstrings
5th rowstrings

ValueCountFrequency (%)
strings62
100.0%
2025-10-30T18:47:59.962847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s124
28.6%
t62
14.3%
r62
14.3%
i62
14.3%
n62
14.3%
g62
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)434
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s124
28.6%
t62
14.3%
r62
14.3%
i62
14.3%
n62
14.3%
g62
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)434
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s124
28.6%
t62
14.3%
r62
14.3%
i62
14.3%
n62
14.3%
g62
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)434
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s124
28.6%
t62
14.3%
r62
14.3%
i62
14.3%
n62
14.3%
g62
14.3%

Report generated by YData.


Table: suppliers

Profile: suppliers

Overview

Brought to you by YData

Dataset statistics
Number of variables19
Number of observations10
Missing cells120
Missing cells (%)63.2%
Total size in memory6.8 KiB
Average record size in memory701.4 B

Variable types
Numeric1
Text6
Unsupported12

Alerts

attachments has constant value ""Constant
__table_name__ has constant value "suppliers"Constant
email_address has 10 (100.0%) missing valuesMissing
business_phone has 10 (100.0%) missing valuesMissing
home_phone has 10 (100.0%) missing valuesMissing
mobile_phone has 10 (100.0%) missing valuesMissing
fax_number has 10 (100.0%) missing valuesMissing
address has 10 (100.0%) missing valuesMissing
city has 10 (100.0%) missing valuesMissing
state_province has 10 (100.0%) missing valuesMissing
zip_postal_code has 10 (100.0%) missing valuesMissing
country_region has 10 (100.0%) missing valuesMissing
web_page has 10 (100.0%) missing valuesMissing
notes has 10 (100.0%) missing valuesMissing
id has unique valuesUnique
company has unique valuesUnique
last_name has unique valuesUnique
first_name has unique valuesUnique
email_address is an unsupported type, check if it needs cleaning or further analysisUnsupported
business_phone is an unsupported type, check if it needs cleaning or further analysisUnsupported
home_phone is an unsupported type, check if it needs cleaning or further analysisUnsupported
mobile_phone is an unsupported type, check if it needs cleaning or further analysisUnsupported
fax_number is an unsupported type, check if it needs cleaning or further analysisUnsupported
address is an unsupported type, check if it needs cleaning or further analysisUnsupported
city is an unsupported type, check if it needs cleaning or further analysisUnsupported
state_province is an unsupported type, check if it needs cleaning or further analysisUnsupported
zip_postal_code is an unsupported type, check if it needs cleaning or further analysisUnsupported
country_region is an unsupported type, check if it needs cleaning or further analysisUnsupported
web_page is an unsupported type, check if it needs cleaning or further analysisUnsupported
notes is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction
Analysis started2025-10-30 16:48:01.007539
Analysis finished2025-10-30 16:48:01.151209
Duration0.14 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size218.0 B
2025-10-30T18:48:01.213112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics
Minimum1
5-th percentile1.45
Q13.25
median5.5
Q37.75
95-th percentile9.55
Maximum10
Range9
Interquartile range (IQR)4.5

Descriptive statistics
Standard deviation3.027650354
Coefficient of variation (CV)0.5504818826
Kurtosis-1.2
Mean5.5
Median Absolute Deviation (MAD)2.5
Skewness0
Sum55
Variance9.166666667
MonotonicityNot monotonic

2025-10-30T18:48:01.296972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
61
10.0%
41
10.0%
71
10.0%
21
10.0%
11
10.0%
51
10.0%
91
10.0%
101
10.0%
31
10.0%
81
10.0%
ValueCountFrequency (%)
11
10.0%
21
10.0%
31
10.0%
41
10.0%
51
10.0%
ValueCountFrequency (%)
101
10.0%
91
10.0%
81
10.0%
71
10.0%
61
10.0%

company
Text

Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size798.0 B
2025-10-30T18:48:01.384529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length10
Median length10
Mean length10
Min length10

Characters and Unicode
Total characters100
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique10 ?
Unique (%)100.0%

Sample
1st rowSupplier F
2nd rowSupplier D
3rd rowSupplier G
4th rowSupplier B
5th rowSupplier A

ValueCountFrequency (%)
supplier10
50.0%
f1
 
5.0%
d1
 
5.0%
g1
 
5.0%
b1
 
5.0%
a1
 
5.0%
e1
 
5.0%
i1
 
5.0%
j1
 
5.0%
c1
 
5.0%
2025-10-30T18:48:01.516527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
p20
20.0%
S10
10.0%
u10
10.0%
l10
10.0%
i10
10.0%
e10
10.0%
r10
10.0%
10
10.0%
F1
 
1.0%
D1
 
1.0%
Other values (8)8
 
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p20
20.0%
S10
10.0%
u10
10.0%
l10
10.0%
i10
10.0%
e10
10.0%
r10
10.0%
10
10.0%
F1
 
1.0%
D1
 
1.0%
Other values (8)8
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p20
20.0%
S10
10.0%
u10
10.0%
l10
10.0%
i10
10.0%
e10
10.0%
r10
10.0%
10
10.0%
F1
 
1.0%
D1
 
1.0%
Other values (8)8
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p20
20.0%
S10
10.0%
u10
10.0%
l10
10.0%
i10
10.0%
e10
10.0%
r10
10.0%
10
10.0%
F1
 
1.0%
D1
 
1.0%
Other values (8)8
 
8.0%

last_name
Text

Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size776.0 B
2025-10-30T18:48:01.604405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length20
Median length8
Mean length7.8
Min length4

Characters and Unicode
Total characters78
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique10 ?
Unique (%)100.0%

Sample
1st rowHayakawa
2nd rowSato
3rd rowGlasson
4th rowWeiler
5th rowAndersen

ValueCountFrequency (%)
hayakawa1
10.0%
sato1
10.0%
glasson1
10.0%
weiler1
10.0%
andersen1
10.0%
hernandez-echevarria1
10.0%
sandberg1
10.0%
sousa1
10.0%
kelley1
10.0%
dunton1
10.0%
2025-10-30T18:48:01.826894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a11
14.1%
e10
12.8%
n8
 
10.3%
r6
 
7.7%
s4
 
5.1%
o4
 
5.1%
l4
 
5.1%
S3
 
3.8%
d3
 
3.8%
i2
 
2.6%
Other values (19)23
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)78
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a11
14.1%
e10
12.8%
n8
 
10.3%
r6
 
7.7%
s4
 
5.1%
o4
 
5.1%
l4
 
5.1%
S3
 
3.8%
d3
 
3.8%
i2
 
2.6%
Other values (19)23
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)78
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a11
14.1%
e10
12.8%
n8
 
10.3%
r6
 
7.7%
s4
 
5.1%
o4
 
5.1%
l4
 
5.1%
S3
 
3.8%
d3
 
3.8%
i2
 
2.6%
Other values (19)23
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)78
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a11
14.1%
e10
12.8%
n8
 
10.3%
r6
 
7.7%
s4
 
5.1%
o4
 
5.1%
l4
 
5.1%
S3
 
3.8%
d3
 
3.8%
i2
 
2.6%
Other values (19)23
29.5%

first_name
Text

Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size768.0 B
2025-10-30T18:48:01.921398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length12
Median length8.5
Mean length7
Min length4

Characters and Unicode
Total characters70
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique10 ?
Unique (%)100.0%

Sample
1st rowSatomi
2nd rowNaoki
3rd rowStuart
4th rowCornelia
5th rowElizabeth A.

ValueCountFrequency (%)
satomi1
8.3%
naoki1
8.3%
stuart1
8.3%
cornelia1
8.3%
elizabeth1
8.3%
a1
8.3%
amaya1
8.3%
mikael1
8.3%
luis1
8.3%
madeleine1
8.3%
Other values (2)2
16.7%
2025-10-30T18:48:02.079046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a10
14.3%
i7
 
10.0%
e6
 
8.6%
l5
 
7.1%
t4
 
5.7%
n3
 
4.3%
r3
 
4.3%
o3
 
4.3%
u3
 
4.3%
k2
 
2.9%
Other values (18)24
34.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)70
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a10
14.3%
i7
 
10.0%
e6
 
8.6%
l5
 
7.1%
t4
 
5.7%
n3
 
4.3%
r3
 
4.3%
o3
 
4.3%
u3
 
4.3%
k2
 
2.9%
Other values (18)24
34.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)70
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a10
14.3%
i7
 
10.0%
e6
 
8.6%
l5
 
7.1%
t4
 
5.7%
n3
 
4.3%
r3
 
4.3%
o3
 
4.3%
u3
 
4.3%
k2
 
2.9%
Other values (18)24
34.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)70
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a10
14.3%
i7
 
10.0%
e6
 
8.6%
l5
 
7.1%
t4
 
5.7%
n3
 
4.3%
r3
 
4.3%
o3
 
4.3%
u3
 
4.3%
k2
 
2.9%
Other values (18)24
34.3%

email_address
Unsupported

Missing  Rejected  Unsupported 

Missing10
Missing (%)100.0%
Memory size208.0 B
Distinct4
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size856.0 B
2025-10-30T18:48:02.142307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length20
Median length19.5
Mean length15.8
Min length13

Characters and Unicode
Total characters158
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique1 ?
Unique (%)10.0%

Sample
1st rowMarketing Assistant
2nd rowMarketing Manager
3rd rowMarketing Manager
4th rowSales Manager
5th rowSales Manager

ValueCountFrequency (%)
sales7
35.0%
manager7
35.0%
marketing3
15.0%
representative2
 
10.0%
assistant1
 
5.0%
2025-10-30T18:48:02.421566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a27
17.1%
e25
15.8%
n13
8.2%
r12
7.6%
s12
7.6%
g10
 
6.3%
M10
 
6.3%
10
 
6.3%
t9
 
5.7%
S7
 
4.4%
Other values (7)23
14.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)158
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a27
17.1%
e25
15.8%
n13
8.2%
r12
7.6%
s12
7.6%
g10
 
6.3%
M10
 
6.3%
10
 
6.3%
t9
 
5.7%
S7
 
4.4%
Other values (7)23
14.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)158
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a27
17.1%
e25
15.8%
n13
8.2%
r12
7.6%
s12
7.6%
g10
 
6.3%
M10
 
6.3%
10
 
6.3%
t9
 
5.7%
S7
 
4.4%
Other values (7)23
14.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)158
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a27
17.1%
e25
15.8%
n13
8.2%
r12
7.6%
s12
7.6%
g10
 
6.3%
M10
 
6.3%
10
 
6.3%
t9
 
5.7%
S7
 
4.4%
Other values (7)23
14.6%

business_phone
Unsupported

Missing  Rejected  Unsupported 

Missing10
Missing (%)100.0%
Memory size208.0 B

home_phone
Unsupported

Missing  Rejected  Unsupported 

Missing10
Missing (%)100.0%
Memory size208.0 B

mobile_phone
Unsupported

Missing  Rejected  Unsupported 

Missing10
Missing (%)100.0%
Memory size208.0 B

fax_number
Unsupported

Missing  Rejected  Unsupported 

Missing10
Missing (%)100.0%
Memory size208.0 B

address
Unsupported

Missing  Rejected  Unsupported 

Missing10
Missing (%)100.0%
Memory size208.0 B

city
Unsupported

Missing  Rejected  Unsupported 

Missing10
Missing (%)100.0%
Memory size208.0 B

state_province
Unsupported

Missing  Rejected  Unsupported 

Missing10
Missing (%)100.0%
Memory size208.0 B

zip_postal_code
Unsupported

Missing  Rejected  Unsupported 

Missing10
Missing (%)100.0%
Memory size208.0 B

country_region
Unsupported

Missing  Rejected  Unsupported 

Missing10
Missing (%)100.0%
Memory size208.0 B

web_page
Unsupported

Missing  Rejected  Unsupported 

Missing10
Missing (%)100.0%
Memory size208.0 B

notes
Unsupported

Missing  Rejected  Unsupported 

Missing10
Missing (%)100.0%
Memory size208.0 B

attachments
Text

Constant 

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size698.0 B

Length
Max length0
Median length0
Mean length0
Min length0

Characters and Unicode
Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st row
2nd row
3rd row
4th row
5th row

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

__table_name__
Text

Constant 

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size788.0 B
2025-10-30T18:48:02.559397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length
Max length9
Median length9
Mean length9
Min length9

Characters and Unicode
Total characters90
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique
Unique0 ?
Unique (%)0.0%

Sample
1st rowsuppliers
2nd rowsuppliers
3rd rowsuppliers
4th rowsuppliers
5th rowsuppliers

ValueCountFrequency (%)
suppliers10
100.0%
2025-10-30T18:48:02.669356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s20
22.2%
p20
22.2%
u10
11.1%
l10
11.1%
i10
11.1%
e10
11.1%
r10
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)90
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s20
22.2%
p20
22.2%
u10
11.1%
l10
11.1%
i10
11.1%
e10
11.1%
r10
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)90
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s20
22.2%
p20
22.2%
u10
11.1%
l10
11.1%
i10
11.1%
e10
11.1%
r10
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)90
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s20
22.2%
p20
22.2%
u10
11.1%
l10
11.1%
i10
11.1%
e10
11.1%
r10
11.1%

Report generated by YData.